Survey on IoT Security: Applications, Threats, Solutions Analysis

This report presents a comprehensive survey on Internet of Things (IoT) security, examining various application areas, security threats, and solution architectures. It begins by highlighting the rapid growth of IoT and the increasing need for robust security measures. The report categorizes different IoT applications, such as smart cities, and discusses the specific security and privacy issues associated with each. It then delves into the sources of threats across different layers of IoT environments, emphasizing the challenges in securing IoT devices compared to traditional IT devices. The paper reviews existing and emerging technologies to enhance IoT security, including blockchain, fog computing, edge computing, and machine learning. These technologies are discussed in detail, along with their potential to improve trust and security in IoT applications. The survey also addresses open issues, challenges, and future research directions for developing secure IoT applications, providing recommendations for improving the IoT infrastructure to facilitate secure communications. The report concludes by emphasizing the critical need for security in all IoT deployments and the importance of addressing the identified challenges to ensure the successful and secure expansion of IoT applications.

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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier xxx
A Survey on IoT Security: Application
Areas, Security Threats, and Solution
Architectures
VIKAS HASSIJA 1, VINAY CHAMOLA2, VIKAS SAXENA 1, DIVYANSH JAIN 1, PRANAV
GOYAL 1 and BIPLAB SIKDAR3
1 Department of CSE and IT, Jaypee Institute of Information Technology, Noida, 201309 India (e-mail: {vikas.hassija, vikas.saxena}@jiit.ac.in), (e-mail:
{divyansh.bjain,pranav.goyal1297}@gmail.com)
2 Department of EEE, Birla Institute of Technology & Science (BITS), Pilani, Rajasthan 333031 India (e-mail: [e-mail])
3 Department of Electrical and Computer Engineering, National University of Singapore, 117583 Singapore (e-mail: [e-mail])
Corresponding author:Vinay Chamola(e-mail: [e-mail]).
ABSTRACT Internet of things (IoT) is the next era of communication. Using IoT, physical objects can
be empowered to create, receive and exchange data in a seamless manner. Various IoT applications focus
on automating different tasks and are trying to empower the inanimate physical objects to act without any
human intervention. The existing and upcoming IoT applications are highly promising to increase the level
of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever
growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it
is imperative to make the required changes in the architecture of IoT applications for achieving end-to-end
secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of
threat in IoT applications is presented. After discussing the security issues, various emerging and existing
technologies focused on achieving a high degree of trust in IoT applications are discussed. Four different
technologies: Blockchain, fog computing, edge computing, and machine learning to increase the level of
security in IoT are discussed.
INDEX TERMS IoT, IoT Security, Blockchain, Fog Computing, Edge Computing, Machine Learning, IoT
applications, Distributed Systems.
I. INTRODUCTION
THE pace of connecting physical devices around us to
the Internet is increasing rapidly. According to a recent
Gartner report, there will be around 8.4 billion connected
things worldwide in 2020. This number is expected to grow
to 20.4 billion by 2022 [1]. The use of IoT applications
is increasing in all parts of the world. The major driving
countries in this include western Europe, North America,
and China [1]. The number of machine to machine (M2M)
connections is expected to grow from 5.6 billion in 2016 to
27 billion in 2024 [1]. This leap in numbers itself declares
IoT to be one of the major upcoming markets that could form
a cornerstone of the expanding digital economy. The IoT
industry is expected to grow in terms of revenue from $892
billion in 2018 to $4 trillion by 2025 [2]. M2M connections
cover a broad range of applications like smart cities, smart
environment, smart grids, smart retail, smart farming, etc.
[3]. Figure 1 shows the past, present and future architecture
of IoT. In future, the devices are not only expected to be
connected to the Internet and other local devices but are also
expected to communicate with other devices on the Internet
directly. Apart from the devices or things being connected,
the concept of social IoT (SIoT) is also emerging. SIoT will
enable different social networking users to be connected to
the devices and users can share the devices over the Internet
[4].
With all this vast spectrum of IoT applications comes
the issue of security and privacy. Without a trusted and
interoperable IoT ecosystem, emerging IoT applications can-
not reach high demand and may lose all their potential.
Along with the security issues faced generally by the Inter-
net, cellular networks, and WSNs, IoT also has its special
security challenges such as privacy issues, authentication
issues, management issues, information storage and so on.
Table 1 summarizes various factors due to which securing
IoT environment is much more challenging than securing
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FIGURE 1: Present and Future Architecture of IoT.
TABLE 1: Comparison of Security of IT devices and IoT devices.
Widespread IT Security IoT security
Widespread IT has devices which is resource rich IoT devices need to be carefully provisioned with security
measures
Widespread IT is based on resource rich devices IoT system are composed of devices having limitation in
terms of their software and hardware
For wide security and lower capabilities complex algorithm
are implemented
only lightweight algorithms are preferred
Homogeneous technology is responsible for high security IoT with heterogeneous technology produce large amount
of heterogeneous data increasing the attack surface
normal information technology (IT) devices. Due to all these
issues and vulnerabilities, the IoT applications create a fertile
ground for different kinds of cyber threats. There have been
various security and privacy attacks on the already deployed
IoT applications worldwide. Mirai attack in the last quarter
of 2016 was estimated to infect around 2.5 million devices
connected to the Internet and launch distributed denial of
service (DDoS) attack [5]. After Mirai, Hajime and Reaper
are the other big botnet attacks launched against a large
number of IoT devices [5]. IoT devices, being low powered
and less secure, provide a gateway to the adversaries for
entering into home and corporate networks, thereby giving
easy access to the user’s data. Also, the domain of IoT is
expanding beyond mere things or objects. There have been
various successful attempts to implant IoT devices into the
human body to monitor the live condition of various organs
[6]. Attackers can target such devices to track the location of
a particular individual or falsify data. Such an attack has not
taken place yet in real life but can be highly dangerous, if
such devices are compromised.
Cyber Physical Systems (CPS) is another area benefitting
from the growth of IoT. In CPS physical objects in the
environment are monitored, and actions are taken based on
the physical changes. Since CPS encompass assets of crit-
ical importance (e.g., power grids, transportation systems),
security vulnerabilities in such systems have serious conse-
quences. However, security challenges for CPS have their
unique characteristics and are outside the scope of this paper.
In any IoT ecosystem or environment, there are four im-
portant layers. The first layer includes the use of various
sensors and actuators to perceive the data or information
to perform various functionalities. Based on that, in the
second layer, a communication network is used to transmit
the collected data. Most of the evolving IoT applications
deploy the third layer, called a middleware layer, to act as
a bridge between the network and application layer. Finally,
on the fourth layer, there are various IoT based end-to-end
applications like smart grids, smart transport, smart factories,
etc. All of these four layers have security problems specific
to them. Apart from these layers, various gateways connect
these layers and help in the data movement. There are certain
security threats specific to these gateways as well.
In this paper, a detailed survey of IoT security solutions in
the existing literature is presented. First of all, the fundamen-
tal constraints to achieve high levels for security in IoT ap-
plications are presented. The goal of this paper is to highlight
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the major existing and upcoming solutions for IoT security.
Specifically, the four major classes of IoT security solutions
namely: (1) blockchain based solutions; (2) fog computing
based solutions; (3) machine learning based solutions and
(4) edge computing based solutions are highlighted. Table 3
gives a list of acronyms related to IoT used in this paper.
A. RELATED SURVEYS AND OUR CONTRIBUTIONS
There are various existing surveys on IoT security and pri-
vacy issues. Yuchen et al. [10] have summarized various
security issues in IoT applications. Authors of [11] have dis-
cussed the security issues specific to location-based services
in IoT. The authors target the particular problems related to
localization and positioning of the IoT devices. Anne et al.
in [12] focus mainly on the security issues related to IoT
middleware and provide a detailed survey of related existing
protocols and their security issues. M. Guizani et al. in [14]
have surveyed various trust management techniques for IoT
along with their pros and cons. Security mechanisms for
IoT security such as software defined networking (SDN)
and network function virtualization (NFV) are discussed
in [13]. In [8] the authors have compared edge computing
with traditional cloud systems to secure IoT systems. Jie
Lin et al. in [9] have discussed the relationship between
IoT and fog computing. Some of the security issues related
to fog computing have also been discussed. Authors of [7]
have discussed vulnerabilities faced by IoT in brief. Table 2
summarizes the main contributions of the previous compre-
hensive surveys on IoT security. Although there are several
works in this direction, they are specific to certain limited
aspects of IoT. This calls the need for a detailed survey
on all the existing and upcoming security challenges in IoT
applications. This paper will help the reader to get a detailed
idea of the state-of-the-art in IoT security and will give them
a general understanding of the area. The main contributions
of this work are as follows:
1. A classification of different IoT applications and spe-
cific security and privacy issues related to those appli-
cations.
2. A detailed explanation of different threat sources in
different layers of IoT.
3. Detailed and realistic recommendations to improve the
IoT infrastructure to facilitate secure communications.
4. Review on the proposed countermeasures to the secu-
rity issues in IoT.
5. An assessment of the open issues, challenges and
future research directions for developing secure IoT
applications.
B. ORGANIZATION
The organization of the rest of the paper is as follows: Section
II describes various application areas of IoT where high
security is required. Section III discusses various sources of
threats in an IoT environment. In section IV various con-
straints and requirements to be considered while developing
a secure IoT application are reviewed. Four major IoT se-
curity approaches, i.e., blockchain, fog computing, machine
learning, and edge computing are presented in Section V,
VI, VII, and VIII, respectively. Section IX describes various
open issues, challenges and upcoming research opportunities
in IoT security and finally, Section X concludes the paper.
II. SECURITY CRITICAL APPLICATION AREAS OF IOT
Security is highly critical in almost all IoT applications that
have already been deployed or are in the process of deploy-
ment. The applications of IoT are increasing very rapidly
and penetrating most of the existing industries. Although
operators support these IoT applications through existing
networking technologies, several of these applications need
more stringent security support from technologies they use.
In this section various security critical IoT applications are
discussed.
1. Smart Cities: Smart cities involve extensive use of
emerging computation and communication resources
for increasing the overall quality of life of the people
[15]. It includes smart homes, smart traffic manage-
ment, smart disaster management, smart utilities, etc.
There is a push to make cities smarter, and govern-
ments worldwide are encouraging their development
through various incentives [16]. Although the use of
smart applications is intended to improve the overall
quality of life of the citizens, it comes with a threat
to the privacy of the citizens. Smart card services tend
to put the card details and purchase behavior of the
citizens at risk. Smart mobility applications may leak
the location traces of the users. There are applications
TABLE 2: Related Surveys on IoT Security
Year Author Contributions
2016 Arsalan Mosenia et al., [7] A brief discussion of vulnerabilities faced by the edge side layer of IoT
2017 Yu wei et al., [8] Survey on using Edge Computing to secure IoT
2017 Jie Lin ea al., [9] Discussion on relationship between IoT and Fog Computing
2017 Y yang et al., [10] A brief discussion on most relevant limitations of IoT devices
2017 L chen , S. Thombre et al., [11] security issues specific to location-based services in IoT
2017 A H Ngu, V. Metsis et al., [12] Security issues related to the IoT middle ware
2018 I Farris, T Taleb et al., [13] Security mechanism for IoT security like SDN and NFB
2019 Ikram Ud din, M. Guizani et al,. [14] Trust Management Techniques for Internet of Things
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TABLE 3: List of Acronyms
Notation Meaning
ABSI Adaptive Binary Splitting Inspection
AMI Advanced Metering Infrastructure
AMQP Advanced Message Queuing Protocol
APT Advanced Persistent Threat
CoAP Constrained Application Protocol
DAC Distributed Autonomous Corporation
DAOs Decentalized Autonomous Organizations
DDoS Distributed denial of service
GPS Global Positioning System
HAN Home Area Network
IIoT Industrial Internet of Things
IOE Internet of Everything
IoT Internet of Things
M2M Machine to Machine
MCC Mobile Cloud Computing
MEC Mobile Edge Computing
MLP Multi-Layer Perceptron
MQTT Message Queuing Telemetry Transport
NFC Near Field Communication
NFV Network Function Virtualization
P2P peer to peer
QoS Quality of Service
RFID Radio Frequency Identification
RSN RFID sensor Networks
SDN Software-Defined Networking
SHA Secure Hash Algorithm
SIoT Social Internet of Things
SMQTT Secure Message Queue Telemetry Transport
STD Security Trust and Decentralization
WSN Wireless Sensor Networks
XMPP Extensible Messaging and Presence Protocol
XSS cross-site scripting
using which parents can keep track of their child. How-
ever, if such applications are hacked, then the safety of
the child can come to risk.
2. Smart Environment: Smart environment includes
various IoT applications such as fire detection in
forests, monitoring the level of snow in high alti-
tude regions, preventing landslides, early detection of
earthquakes, pollution monitoring, etc. All these IoT
applications are closely related to the life of human
beings and animals in those areas. The government
agencies involved in such fields will also be relying on
the information from these IoT applications. Security
breaches and vulnerability in any area related to such
IoT applications can have serious consequences. In this
context, both false negatives and false positives can
lead to disastrous results for such IoT applications. For
example, if the application starts detecting earthquakes
falsely, then it will lead to monetary losses for the
government and businesses. On the other hand, if the
application is not able to predict the earthquake, then
it will lead to the loss of both property and life. There-
fore, smart environment applications have to be highly
precise, and security breaches and data tampering must
be avoided.
3. Smart Metering and Smart Grids: Smart metering
includes applications related to various measurements,
monitoring, and management. The most common ap-
plication of smart metering is smart grids, where the
electricity consumption is measured and monitored.
Smart metering may also be used to address the prob-
lem of electricity theft [17]. Other applications of smart
metering include monitoring of water, oil and gas
levels in storage tanks and cisterns. Smart meters are
also used to monitor and optimize the performance of
solar energy plants by dynamically changing the angle
of solar panels to harvest the maximum possible solar
energy. There also exist some IoT applications that use
smart meters to measure the water pressure in water
transport systems or to measure the weight of goods.
However, smart metering systems are vulnerable to
both physical and cyber-attacks as compared to analog
meters that can be tampered only by physical attacks.
Also, smart meters or advanced metering infrastruc-
ture (AMI) are intended to perform functions beyond
generic energy usage recording. In a smart home area
network (HAN) all electric equipment at home are con-
nected to smart meters and the information collected
from these equipments can be used for load and cost
management. Intentional intrusion in such communi-
cation systems by the consumer or an adversary may
modify the collected information, leading to monetary
loss for the service providers or consumers [18].
4. Security and Emergencies: Security and emergencies
is another important area where various IoT applica-
tions are being deployed. It includes applications such
as allowing only authorized people in restricted areas
etc. Another application in this domain is the detection
of leakage of hazardous gases in industrial areas or
areas around chemical factories. Radiation levels can
also be measured in the areas around nuclear power
reactors or cellular base stations and alerts can be
generated when the radiation level is high. There are
various buildings whose systems have sensitive data or
that house sensitive goods. Security applications can
be deployed to protect sensitive data and goods. IoT
applications that detect various liquids can also be used
to prevent corrosion and break downs in such sensitive
buildings. Security breaches in such applications can
also have various serious consequences. For example,
the criminals may try to enter the restricted areas by
attacking the vulnerabilities in such applications. Also,
false radiation level alarms can have serious immediate
and long term impacts. For example, if infants are
exposed to high levels of radiation, then it may lead
to serious life threatening diseases in long term.
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5. Smart Retail: IoT applications are being extensively
used in the retail sector. Various applications have been
developed to monitor the storage conditions of the
goods as they move along the supply chain. IoT is also
being used to control the tracking of products in the
warehouses so that restocking can be done optimally.
Various intelligent shopping applications are also being
developed for assisting the customers based on their
preferences, habits, allergies to certain components,
etc. Mechanisms to provide the experience of online
shopping to offline retailers using augmented reality
techniques have also been developed. Various compa-
nies in retail have faced security issues in deploying
and using various IoT applications. Some of these com-
panies include Apple, Home Depot, JP Morgan Chase
and Sony [19]. Adversaries may try to compromise the
IoT applications associated with storage conditions of
the goods and may try to send wrong information about
the products to the users in order to increase the sale.
If security features are not implemented in smart retail,
attackers may steal debit and credit card information,
phone numbers, email-addresses, etc. of the customers
which can lead to monetary losses for the customers
and retailers.
6. Smart Agriculture and Animal Farming: Smart
agriculture includes monitoring soil moisture, control-
ling micro-climate conditions, selective irrigation in
dry zones, and controlling humidity and temperature.
Usage of such advanced features in agriculture can help
in achieving high yields and can save farmers from
monetary losses. Control of temperature and humidity
levels in various grain and vegetable production can
help in preventing fungus and other microbial contam-
inants. Controlling the climate conditions can also help
in increasing the vegetable and crop yield and quality.
Just like crop monitoring, there are IoT applications to
monitor the activities and the health condition of farm
animals by attaching sensors to the animals. If such
applications are compromised, then it may lead to the
theft of animals from the farm and adversaries may also
damage the crops.
7. Home Automation: Home automation is one of the
most widely used and deployed IoT applications. This
includes applications such as those for remotely con-
trolling electrical appliances to save energy, systems
deployed on windows and doors to detect intruders, etc.
Monitoring systems are being applied to track energy
and water supply consumption, and users are being ad-
vised to save cost and resources. Authors in [20] have
proposed the use of logic based security algorithms
to enhance security level in homes. Intrusions are de-
tected by comparing the user actions at key locations
of the home with normal behavior of the user in these
locations. However, attackers may gain unauthorized
access of the IoT devices in the home and try to harm
the users. For instance, cases of home burglaries have
FIGURE 2: Layers in IoT System.
increased rapidly after the deployment of various home
automation systems [20]. There have also been various
cases in the past where the adversaries try to analyze
the type and volume of Internet traffic to/from the smart
home for judging the behavior and presence of the
residents.
III. SOURCES OF SECURITY THREATS IN IOT
APPLICATIONS
As discussed in Section I, any IoT application can be divided
into four layers: (1) sensing layer; (2) network layer; (3) mid-
dleware layer; and (4) application layer. Each of these layers
in an IoT application uses diverse technologies that bring a
number of issues and security threats. Figure 2 shows various
technologies, devices, and applications at these four layers.
This section discusses various possible security threats in
IoT applications for these four layers. Figure 3 shows the
possible attacks on these four layers. The special security
issues associated with the gateways that connect these layers
are also discussed in this section.
A. SECURITY ISSUES AT SENSING LAYER
The sensing layer mainly deals with physical IoT sensors and
actuators. Sensors sense the physical phenomenon happening
around them [21]–[23]. Actuators, on the other hand, perform
a certain action on the physical environment, based on the
sensed data. There are various kinds of sensors for sens-
ing different kinds of data, e.g., ultrasonic sensors, camera
sensors, smoke detection sensors, temperature and humidity
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FIGURE 3: Types of Attacks on IoT.
sensors, etc. There can be mechanical, electrical, electronic
or chemical sensors used to sense the physical environment.
Various sensing layer technologies are used in different IoT
applications like RFID, GPS, WSNs, RSNs, etc. Major secu-
rity threats that can be encountered at the sensing layer are as
follows:
1. Node Capturing: IoT applications comprise of sev-
eral low power nodes such as sensors and actuators.
These nodes are vulnerable to a variety of attacks by
the adversaries. The attackers may try to capture or
replace the node in the IoT system with a malicious
node. The new node may appear to be the part of
the system but is controlled by the attacker. This may
lead to compromising the security of the complete IoT
application [24].
2. Malicious Code Injection Attack: The attack in-
volves the attacker injecting some malicious code in
the memory of the node. Generally, the firmware or
software of IoT nodes are upgraded on the air, and
this gives a gateway to the attackers to inject malicious
code. Using such malicious code, the attackers may
force the nodes to perform some unintended functions
or may even try to access the complete IoT system.
3. False Data Injection Attack: Once the node is cap-
tured, the attacker may use it to inject erroneous data
onto the IoT system. This may lead to false results and
may result in malfunctioning of the IoT application.
The attacker may also use this method to cause a DDoS
attack.
4. Side-Channel Attacks (SCA): Apart from direct at-
tacks on the nodes, various side-channel attacks may
lead to leaking of sensitive data. The microarchitec-
tures of processors, electromagnetic emanation and
their power consumption reveal sensitive information
to adversaries. Side channel attacks may be based on
power consumption, laser-based attacks, timing attacks
or electromagnetic attacks. Modern chips take care of
various countermeasures to prevent these side-channel
attacks while implementing the cryptographic mod-
ules.
5. Eavesdropping and Interference: IoT applications
often consist of various nodes deployed in open envi-
ronments [25]. As a result, such IoT applications are
exposed to eavesdroppers. The attackers may eaves-
drop and capture the data during different phases like
data transmission or authentication.
6. Sleep Deprivation Attacks: In such type of attacks the
adversaries try to drain the battery of the low-powered
IoT edge devices. This leads to a denial of service
from the nodes in the IoT application due to a dead
battery. This can be done by running infinite loops in
the edge devices using malicious code or by artificially
increasing the power consumption of the edge devices.
7. Booting Attacks: The edge devices are vulnerable to
various attacks during the boot process. This is because
the inbuilt security processes are not enabled at that
point. The attackers may take advantage of this vul-
nerability and try to attack the node devices when they
are being restarted. As edge devices are typically low
powered and at times go through sleep-wake cycles,
it is thus essential to secure the boot process in these
devices.
B. SECURITY ISSUES AT NETWORK LAYER
The key function of the network layer is transmitting the
information received from the sensing layer to the computa-
tional unit for processing. The major security issues that are
encountered at the network layer are as follows.
1. Phishing Site Attack: Phishing attacks often refer to
attacks where several IoT devices can be targeted by a
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minimal effort put by the attacker. The attackers expect
that at least few of the devices will become a victim of
the attack. There is a possibility of encountering phish-
ing sites in the course of users visiting web pages on
the Internet. Once the user’s account and password are
compromised, the whole IoT environment being used
by the user becomes vulnerable to cyber attacks. The
network layer in IoT is highly vulnerable to phishing
sites attacks [26].
2. Access Attack: Access attack is also referred to as ad-
vanced persistent threat (APT). This is a type of attack
in which an unauthorized person or an adversary gains
access to the IoT network. The attacker can continue
to stay in the network undetected for a long duration.
The purpose or intention of this kind of attack is to
steal valuable data or information, rather than to cause
damage to the network. IoT applications continuously
receive and transfer valuable data and are therefore
highly vulnerable to such attacks [27].
3. DDoS/DoS Attack: In this kind of attacks, the attacker
floods the target servers with a large number of un-
wanted requests. This incapacitates the target server,
thereby disrupting services to genuine users. If there
are multiple sources used by the attacker to flood the
target server, then such an attack is termed as DDoS
or distributed denial of service attack. Such attacks are
not specific to IoT applications, but due to the hetero-
geneity and complexity of IoT networks, the network
layer of the IoT is prone to such attacks. Many IoT
devices in IoT applications are not strongly configured,
and thus become easy gateways for attackers to launch
DDoS attacks on the target servers. The Mirai botnet
attack as discussed in Section I used this vulnerability
and blocked various servers by constantly propagating
requests to the weakly configured IoT devices [28].
4. Data Transit Attacks: IoT applications deal with a
lot of data storage and exchange. Data is valuable, and
therefore it is always the target of hackers and other ad-
versaries. Data that is stored in the local servers or the
cloud has a security risk, but the data that is in transit
or is moving from one location to another is even more
vulnerable to cyber attacks. In IoT applications, there
is a lot of data movement between sensors, actuators,
cloud, etc. Different connection technologies are used
in such data movements, and therefore IoT applications
are susceptible to data breaches.
5. Routing Attacks: In such attacks, malicious nodes
in an IoT application may try to redirect the routing
paths during data transit. Sinkhole attacks are a specific
kind of routing attack in which an adversary advertises
an artificial shortest routing path and attracts nodes to
route traffic through it. A worm-hole attack is another
attack which can become serious security threat if
combined with other attacks such as sinkhole attacks.
A warm-hole is an out of band connection between two
nodes for fast packet transfer. An attacker can create a
warm-hole between a compromised node and a device
on the internet and try to bypass the basic security
protocols in an IoT application.
C. SECURITY ISSUES AT MIDDLEWARE LAYER
The role of the middleware in IoT is to create an abstraction
layer between the network layer and the application layer.
Middleware can also provide powerful computing and stor-
age capabilities [29]. This layer provides APIs to fulfill the
demands of the application layer. Middleware layer includes
brokers, persistent data stores, queuing systems, machine
learning, etc. Although the middleware layer is useful to
provide a reliable and robust IoT application, it is also sus-
ceptible to various attacks. These attacks can take control
of the entire IoT application by infecting the middleware.
Database security and cloud security are other main security
challenges in the middleware layer. Various possible attacks
in the middleware layer are discussed as follows.
1. Man-in-the-Middle Attack: The MQTT protocol
uses publish-subscribe model of communication be-
tween clients and subscribers using the MQTT bro-
ker, which effectively acts as a proxy. This helps in
decoupling the publishing and the subscribing clients
from each other and messages can be sent without the
knowledge of the destination. If the attacker can con-
trol the broker and become a man-in-the-middle, then
he/she can get complete control of all communication
without any knowledge of the clients.
2. SQL Injection Attack: MIddleware is also suscep-
tible to SQL Injection (SQLi) attacks. In such at-
tacks, attacker can embed malicious SQL statements
in a program [30], [31]. Then, the attackers can obtain
private data of any user and can even alter records
in the database [32]. Open Web Application Security
Project (OWASP) has listed SQLi as a top threat to web
security in their OWASP top 10 2018 document [33].
3. Signature Wrapping Attack: In the web services
used in the middleware, XML signatures are used [34].
In a signature wrapping attack, the attacker breaks
the signature algorithm and can execute opera-
tions or modify eavesdropped message by exploit-
ing vulnerabilities in SOAP (Simple Object Access
Protocol) [35].
4. Cloud Malware Injection: In cloud malware injec-
tion, the attacker can obtain control, inject malicious
code or can inject a virtual machine into the cloud.
The attacker pretends to be a valid service by trying
to create a virtual machine instance or a malicious
service module. In this way, the attacker can obtain
access to service requests of the victim’s service and
can capture sensitive data which can be modified as per
the instance.
5. Flooding Attack in Cloud: This attack works almost
the same as DoS attack in the cloud and affects the
quality of service (QoS). For depleting cloud resources,
the attackers continuously send multiple requests to a
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service. These attacks can have a big impact on cloud
systems by increasing the load on the cloud servers.
D. SECURITY ISSUES AT GATEWAYS
Gateway is a broad layer that has an important role in con-
necting multiple devices, people, things and cloud services.
Gateways also help in providing hardware and software
solutions for IoT devices. Gateways are used for decrypting
and encrypting IoT data and translating protocols for com-
munication between different layers [36]. IoT systems today
are heterogeneous including LoraWan, ZigBee, Z-Wave and
TCP/IP stacks with many gateways in between. Some of the
security challenges for IoT gateway are discussed below.
1. Secure On-boarding: When a new device or sensor is
installed in an IoT system, it is imperative to protect
encryption keys. Gateways act as an intermediary be-
tween the new devices and the managing services, and
all the keys pass through the gateways. The gateways
are susceptible to man-in-the-middle attacks and eaves-
dropping to capture the encryption keys, especially
during the on-boarding process.
2. Extra Interfaces: Minimizing the attack surface is an
important strategy that needs to be kept in mind while
installing the IoT devices [37]. Only the necessary
interfaces and protocols should be implemented by
an IoT gateway manufacturer. Some of the services
and functionalities should be restricted for end-users to
avoid backdoor authentication or information breach.
3. End-to-End Encryption: True end-to-end application
layer security is required to ensure the confidentiality
of the data [38]. The application should not let anyone
other than the unique recipient to decrypt the encrypted
messages. Although Zigbee and Zwave protocols sup-
port encryption, this is not end-to-end encryption, be-
cause, in order to translate the information from one
protocol to another, the gateways are required to de-
crypt and re-encrypt the messages. This decryption at
the gateway level makes the data susceptible to data
breaches.
4. Firmware updates: Most IoT devices are resource
constrained, and therefore they do not have an user
interface or the computation power to download and
install the firmware updates. Generally, gateways are
used to download and apply the firmware updates.
The current and new version of the firmware should
be recorded, and validity of the signatures should be
checked for secure firmware updates.
E. SECURITY ISSUES AT APPLICATION LAYER
The application layer directly deals with and provides ser-
vices to the end users. IoT applications like smart homes,
smart meters, smart cities, smart grids, etc. lie in this layer.
This layer has specific security issues that are not present
in other layers, such as data theft and privacy issues. The
security issues in this layer are also specific to different
applications. Many IoT applications also consist of a sub-
layer between the network layer and application layer, usu-
ally termed as an application support layer or middleware
layer. The support layer supports various business services
and helps in intelligent resource allocation and computation.
Major security issues encountered by the application layer
are discussed below.
1. Data Thefts: IoT applications deal with lot of critical
and private data. The data in transit is even more
vulnerable to attacks than data at rest, and in IoT
applications, there is a lot of data movement. The users
will be reluctant to register their private data on IoT
applications if these applications are vulnerable to data
theft attacks. Data encryption, data isolation, user and
network authentication, privacy management, etc. are
some of the techniques and protocols being used to
secure IoT applications against data thefts.
2. Access Control Attacks: Access control is authoriza-
tion mechanism that allows only legitimate users or
processes to access the data or account. Access control
attack is a critical attack in IoT applications because
once the access is compromised, then the complete IoT
application becomes vulnerable to attacks.
3. Service Interruption Attacks: These attacks are also
referred to as illegal interruption attacks or DDoS
attacks in existing literature. There have been various
instances of such attacks on IoT applications. Such
attacks deprive legitimate users from using the services
of IoT applications by artificially making the servers or
network too busy to respond.
4. Malicious Code Injection Attacks: Attackers gener-
ally go for the easiest or simplest method they can use
to break into a system or network. If the system is
vulnerable to malicious scripts and misdirections due
to insufficient code checks, then that would be the first
entry point that an attacker would choose. Generally,
attackers use XSS (cross-site scripting) to inject some
malicious script into an otherwise trusted website. A
successful XSS attack can result in the hijacking of an
IoT account and can paralyze the IoT system.
5. Sniffing Attacks: The attackers may use sniffer appli-
cations to monitor the network traffic in IoT applica-
tions. This may allow the attacker to gain access to
confidential user data if there are not enough security
protocols implemented to prevent it [39].
6. Reprogram Attacks: If the programming process is
not protected, then the attackers can try to reprogram
the IoT objects remotely. This may lead to the hijack-
ing of the IoT network [40].
IV. IMPROVEMENTS AND ENHANCEMENTS REQUIRED
FOR UPCOMING IOT APPLICATIONS
Personal computers (PC) and smartphones have a number
of security features built into them, e.g., firewalls, anti-
virus softwares, address space randomization, etc. These
safety shields are, in general, missing in various IoT devices
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FIGURE 4: Research papers addressing IoT security using various security techniques.
that are already in the market. There are various security
challenges that the IoT applications are facing currently. A
well-defined framework and standard for an end-to-end IoT
application is not yet available. An IoT application is not a
standalone application, and it is an assembled product which
includes work from many individuals and industries. At every
layer starting from sensing to the application, several diverse
products and technologies are being used. These include a
large number of sensors and actuators at the edge nodes.
There are multiple communication standards like cellular
network, WiFi, IEEE 802.15.4, Insteon, dash7, Bluetooth,
etc. A handshake mechanism is required between all these
standards. Apart from this, various connectivity technologies
are being used at different levels in the same IoT application
like Zigbee, 6LOWPAN, wireless HART, Z-Wave, ISA100,
Bluetooth, NFC, RFID, etc. Over and above this, the generic
HTTP protocol cannot be used in the application layer. HTTP
is not suitable for resource-constrained environments because
it is heavy-weight and thus incurs a large parsing overhead.
Therefore, at the application layer also there are many alter-
nate protocols that have been deployed for IoT environments.
Some of them are MQTT, SMQTT, CoAP, XMPP, AMQP,
M3DA, JavascriptIoT, etc.
Due to the intense diversity of protocols, technologies,
and devices in an IoT application, the significant trade-offs
are between cost effectiveness, security, reliability, privacy,
coverage, latency, etc. If one metric for improvement is
optimized, it may result in the degradation of other metric.
For example, imposing too many security checks and proto-
cols in all data transactions in IoT applications may end up
increasing the cost and latency of the application, thereby,
making it unsuitable for the users.
A typical IoT application consists of a big chain of
connected devices, technologies, domains, and geographies.
Even if one of the device or technology or their combination
is left weak, then that may be the cause of a security threat
for the entire application. The chain is considered to be as
strong as the weakest link. There has been a large increase
in the number of weak links in IoT applications recently. For
example, even basic IoT applications such as smart bulbs and
smart door locks can be used as a weak link in a smart home
IoT application to extract the user’s WiFi password [41] and
[42].
The large number of IoT devices being deployed around
the world to make it smart generates a large amount of en-
vironment and user-related data. A lot of private information
can be inferred from this data, and that can be another cause
of threat for an individual and society at large [7]. As a result,
significant improvements and enhancements in the current
IoT application structure and framework are required to make
it reliable, secure and robust. In this regard:
1. Rigorous penetration testing for IoT devices is nec-
essary to quantify the level of risk involved in de-
ploying these devices in different applications. Based
on the risk involved, a priority list can be made and
the devices can be deployed appropriately in different
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applications.
2. Encryption techniques are being used in IoT system at
different layers and protocols. However, there are var-
ious levels of encrypt, decrypt, and re-encrypt cycles
in the complete system. These cycles make the system
vulnerable to attacks. End to end encryption would be
a promising solution to prevent different attacks.
3. Authenticate-always protocols need to be imple-
mented. Whenever a device wants to interact with
another device, an authentication process should be
implemented. Digital certificates can be a promising
solution to provide seamless authentication with bound
identities that are tied to cryptographic protocols.
4. Any IoT security framework being implemented
should be tested and confirmed for scalability. The
security protocols should not be working only for a
limited set of users. The real threats start coming only
when the application becomes public and starts being
used widely in the public domain. Therefore, proper
strategy and planning are required.
5. A mechanism based on encryption techniques like
RSA, SHA256, or hash chains is required to secure the
user and environment data from being captured. IoT
devices need to be designed in a way that they can
transmit the sensed data in a secure and encrypted way.
This will help in gaining the trust of the individuals,
government agencies and industries in IoT applica-
tions.
6. Since the IoT devices and applications are growing
rapidly, an approach needs to be designed to handle
the cost and capacity constraints that are expected
to be encountered shortly. A paradigm shift from a
centralized approach to some decentralized approach
might be needed, where devices can automatically and
securely communicate with each other. This can help
in reducing the cost of managing the applications and
can reduce the issues of capacity constraints [43].
7. Since most of the IoT applications use cloud services
for data storage and retrieval, the risks caused by the
cloud should also be considered. Cloud is a public
platform used by multiple users and there may be
malicious users on the cloud who can be the cause of
threat for IoT related data. The data should be stored
as ciphertext in the cloud and the cloud should not
be allowed to decrypt any ciphertext. This can further
enhance data security and can save us from the generic
risks of using cloud services [44].
8. Apart from the challenges from outside entities, there
are various scenarios where the sensors in an IoT appli-
cation start collecting or sending erroneous data. These
errors might be easy to handle in case of a centralized
architecture but can become a bottleneck in case of an
autonomous decentralized architecture. Faulty reading
or transmitting of data can lead to undesirable results.
Thus, mechanism needs to be identified to validate the
data flow, especially in case of a distributed architec-
ture [45].
9. Since the ultimate goal of all IoT applications is to cre-
ate an autonomous system that needs minimum human
interventions, the use of some artificial intelligence
(AI) based techniques or algorithms to secure IoT
devices might be useful. This can help in reducing the
analysis and communication load on IoT environment
[46].
There are various techniques and approaches in the ex-
isting literature for securing IoT environments and applica-
tions. These solutions may be divided into four categories:
(1) blockchain based solutions; (2) fog computing based
solutions; (3) machine learning based solutions and (4) edge
computing based solutions. Figure 4 shows various works
in different domains that have used the above-mentioned
solutions for securing the IoT environments [47]–[97]. In the
following sections, these solutions are described in detail.
V. IOT SECURITY USING BLOCKCHAIN
Blockchain and IoT are important technologies that will
have a high impact on the IT and communication industry.
These two technologies focus on improving the overall trans-
parency, visibility, level of comfort and level of trust for the
users. The IoT devices provide real-time data from sensors
and blockchain provides the key for data security using a
distributed, decentralized and shared ledger [108].
The basic idea behind the blockchain is simple: it is a dis-
tributed ledger (also called replicated log files). The entries
in the blockchain are chronological and time-stamped. Each
entry in the ledger is tightly coupled with the previous entry
using cryptographic hash keys. A Merkle tree is used to store
the individual transactions and the root hash of the tree is
stored in the blockchain. In the figure, T 1, T 2, T 3, · · · , T n
represent the individual transactions. The transactions are
cryptographically hashed and stored on the leaf nodes of the
tree as Ha, Hb, Hcand so on. The hash of the child nodes
are concatenated and a new root hash is generated. The final
root hash (e.g., H1andH2) is stored on the blockchain. Just
the root hash can be verified in order to make sure that all
the transactions associated with that root hash are secure and
have not been tampered with. Even if a single transaction
is changed, all the hash values on that particular side of the
tree will change. The ledger maintainer or the miner verifies
the logs or transactions and generates a key that enables the
latest transaction to become the part of complete ledger. This
process makes the latest entries available to all the nodes in
the network. Due to the presence of cryptographic hash keys
in each block, it is too time-consuming and difficult for the
adversaries to tamper with the blocks [109].
The miners do not have any personal interest in the trans-
actions, and they are mining just to earn their incentives. The
miners do not know the identity of the owners of the transac-
tions. Over and above, there are multiple miners working on
the same set of transactions, and there is a strong competition
between them to add the transactions to the blockchain. All
these unique features empower the blockchain to be a strong,
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FIGURE 5: Working process of Blockchain
tamper-proof, distributed and open data structure for IoT data
[110]. Figure 5 shows the complete flow of a transaction from
being initialized to being committed to the distributed chain.
There are various platforms and frameworks being developed
in academia and industry that support the creation and main-
tenance of blockchain. Some examples of such platforms are
Ethereum, Hyperledger fabric, Ripple, etc. [111].
A. PERMISSIONED AND PERMISSION-LESS
BLOCKCHAIN
There are two types of blockchain architectures based on
the type of data being added and the nature of application
using blockchain. In permission-less blockchain, there is no
specific permission required for a user to become the part
of the blockchain network or to become a miner. Anyone
can join or leave this network of permission-less blockchain.
The best example of permission-less blockchains is Bitcoin.
Although the throughput of transactions is not very high, the
TABLE 4: Challenges in IoT and Possible Blockchain Solution
Challenge Towards IoT Specification Possible Blockchain Solution
Privacy in IoT devices IoT devices are vulnerable to exposing
private user data
To address such a challenge, pro-
posed solution is to use Permissioned
Blockchain that can secure the IoT de-
vices [98], [99], [100].
Cost and traffic To handle exponential growth in IoT
devices
Moving towards decentralization us-
ing blockchian. The devices can di-
rectly connect and communicate with
the peers rather than communicating
via central servers [3], [101], [102].
Heavy load on cloud service and ser-
vices insufficiency
Cloud services are unavailable due to
attacks, bugs in software, power or
other problems
Records are updated on different nodes
on the network that hold same data so
there is no single point of failure [103],
[104].
Defective architecture All parts of IoT devices have point
of failure that affects network and the
whole device
Validity of devices is verified due to
blockchain. The data is also verified
cryptographically to ensure that only
main originator can send it [105].
Data manipulation Data is extracted from IoT devices and
after manipulating, the data it is used in
some inappropriate way
Due to blockchain, devices are inter-
locked. If any device updates data the
system rejects it [106], [107].
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FIGURE 6: Basic Blockchain Architecture.
permission-less blockchains can support a large number of
nodes in the network.
On the other hand, the permissioned blockchains have a
defined set of rules to participate in the blockchain network.
The miners are also the authorized persons and the blocks are
allowed to be added to the chain only after their validation.
The blockchain of Ripple and Hyperledger are two prime
examples of permissioned blockchain. The permissioned
concept of blockchain improves the overall throughput of
transactions as compared to permission-less blockchains.
Figure 6 shows the sample architecture of a blockchain and
the way every block is connected to all the previous blocks
based on cryptographic hashing.
B. BENEFITS OF BLOCKCHAIN IN IOT
The usage of blockchain has many advantages in IoT appli-
cations. Table 4 gives a summary of some specific challenges
in IoT security and their possible solutions using blockchain.
Various security issues faced by IoT applications have al-
ready been discussed in Section III. The key benefits of using
blockchain in IoT applications are discussed below.
1. Data coming from IoT devices can be stored in
Blockchain: The IoT applications include a large va-
riety of devices connected to each other. These devices
are further connected and controlled by other devices.
This setup is further connected to the cloud to enable
IoT applications to be used from any location. Due
to this large space for data movement, blockchain is
a promising solution to store the data and prevent it
from being misused. Irrespective of the layer in an IoT
application, blockchain can act as a suitable solution to
store and transmit data.
2. Distributed nature of blockchain allowing secure
data storage: Since the blockchain architecture is
distributed in nature, it can avoid the risk of being
a single point of failure as is faced by various IoT
applications based on the cloud. Irrespective of the
distance between the devices, the data generated by
them can be easily stored on the blockchain in a secure
manner [112].
3. Data encryption using the hash key and verified by
miners: In blockchain, only the 256-bit hash key for
the data can be stored, rather than storing the actual
data. The actual data can be stored on the cloud and
the hash key can be mapped with the original data. If
there is any change in the data, the hash of the data
will change. This makes the data secure and private.
The size of blockchain will also not get affected by the
size of the data as only the hash values are stored in the
chain. Only the intended parties, who are authorized
to use that data can access the data from the cloud
using the hash of the data. Every set of data being
stored on blockchain is properly verified by different
miners in the network, and therefore the probability of
storing corrupt data from the devices reduces by using
blockchain as a solution.
4. Prevention from data loss and spoofing attacks: In
spoofing attacks on IoT applications, a new adversary
node enters into the IoT network and starts imitating
to be the part of the original network. By spoofing, the
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adversary can easily capture, observe or inject data in
the network. Blockchain acts as a promising solution
to prevent such attacks. Each legitimate user or device
is registered on blockchain, and devices can easily
identify and authenticate each other without the need
for central brokers or certification authorities [113].
Being low powered in nature, IoT devices inherit the
risk of losing data. There might be cases where due to
some external environmental issues the data is lost by
both the sender and the receiver. Use of blockchain can
prevent such losses as once the block is added in the
chain there is no way to remove it [114].
5. Blockchain to prevent unauthorized access: Many
IoT applications involve a lot of frequent communi-
cation between various nodes. The communication in
blockchain takes place using the public and private
keys, and therefore only the intended party or node
can access the data. Even if the unintended party is
able to access the data, the contents of the data will be
incomprehensible as the data is encrypted with keys.
Therefore, the blockchain data structure tries to handle
various security issues faced by IoT applications.
6. Proxy-based architecture in blockchain for resource-
constrained devices: Although blockchain provides
various security features for a distributed environment,
IoT has a specific challenge of resource constraints.
Being highly resource-constrained, IoT devices cannot
store large ledgers. There have been various works
in this direction to facilitate the use of blockchain in
IoT. Proxy-based architecture is one of the promising
solutions that can help IoT devices to use blockchain.
Proxy servers can be deployed in the network, to store
the resources in an encrypted form. The encrypted
resources can be downloaded by the client from the
proxy servers [115].
7. Elimination of centralized cloud servers: Blockchain
can enhance the security of IoT systems because it
ultimately eliminates the centralized cloud servers and
makes the network peer-to-peer. Centralized cloud
servers are the prime target of the data thieves. Using
blockchain, the data will be distributed among all the
nodes of the network and will be encrypted using a
cryptographic hash function.
C. MERKLE TREE
Merkle tree is an add-on that can be added to the blockchain
data structure to enhance the security of IoT devices. This
can also help in reducing the overall number of blocks being
added in the chain. A Merkle tree is like a binary tree where
every node contains two child nodes except the leaf nodes.
The leaf nodes contain the data or transactions, and the roots
are the hash values of the data on the leaf nodes [116]. Based
on the size of the tree, multiple transactions can be combined
to generate a single root hash. Rather than treating each
transaction as a block, each root hash can be considered as a
block in the chain. This can help us in reducing the number of
blocks. Also, due to multiple levels of hashing, at every level
in the tree, the security of the data is enhanced [117]. IoT
devices involve a lot of small communications among each
other and therefore using Merkle tree along with blockchain
can be a promising solution [118].
D. IOTA
IOTA is another upcoming and highly promising solution
to secure IoT. IOTA is also a DLT (Distributed Ledger
Technology) as blockchain. IOTA is specially designed for
resource-constrained IoT devices. Every incoming request in
the network is required to validate the previous two requests.
Using this process of cumulative validations, IOTA can pro-
vide a high level of security at the device or edge level.
The tip selection algorithm is used for request verification.
A cumulative weight is created for all requests. Higher the
weight of a device in the network, more secure the device is.
IOTA uses a tangle data structure as compared to the chain
data structure in blockchain [119].
VI. IOT SECURITY USING FOG COMPUTING
A. EVOLUTION OF FOG FROM CLOUD
IoT and cloud computing are two independent technologies
which have many applications. IoT has provided users with
a large number of smart devices and applications. Simi-
larly, a cloud provides a very effective solution to store and
manage data which can be accessed from anywhere and is
widely used by many organizations. IoT is generating an
unprecedented amount of data, which puts a lot of strain on
the Internet infrastructure. The integration of cloud and IoT
has introduced an era of new opportunities and challenges
for processing, storing, managing and securing data more
effectively. Industry and research groups have tried to solve
some issues faced by the IoT by integrating it with the cloud.
The benefits of this integration are not enough to address
all the issues faced by IoT. Therefore, the concept of fog
computing was introduced by Cisco in 2012. Fog computing
complements cloud computing rather than replacing it.
B. FOG COMPUTING ARCHITECTURE
The main task of fog computing is to handle the data gen-
erated by IoT devices locally for better management and
thus requires an architecture consisting of different layers.
It has two frameworks that are Fog-Device framework and
Fog-Cloud-Device framework [120]. The former framework
consists of device and fog layer and the latter framework
consists of device, fog and cloud layer. The arrangement
of layers is done based on their storing and computational
powers. The communication between different layers is
done using wired (e.g., optical fiber, Ethernet) or wireless
communication (e.g., WiFi, Bluetooth, etc.). In Fog-Device
framework, the fog nodes provide various services to a user
without involving cloud servers. However, in Fog-Cloud-
Device framework the simple decisions are taken at the fog
layer, whereas, the complex decisions are taken on cloud
[121]. The architecture of Fog-Cloud-Device framework is
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FIGURE 7: Fog Computing Architecture.
shown in Figure 7. The authors of [122] have considered
the fog computing architecture theoretically and mathemat-
ically while comparing the performance of fog computing
paradigm with traditional cloud computing framework based
on service latency and energy consumption. Fog computing
reduces the data traffic between cloud and network edge by
90% and average response time for a user by 20% when
compared with cloud-only model [123]. Authors in [124]
have discussed the definition and concept of fog computing
in-depth, comparing it with similar concepts such as mobile-
edge computing (MEC) and mobile cloud computing (MCC).
Authors in [124] have also introduced some applications like
real-time video analytics and augmented reality (AR), mobile
big data analytics, and content delivery and caching for fog
computing.
C. ADVANTAGES OF FOG OVER CLOUD
IoT devices generate large volumes of data every day. Mov-
ing this data to the cloud in real-time for analysis is not
feasible. Therefore, the concept of fog computing has been
developed. Figure 7 shows the placement and functionality
of fog layer in an IoT application. Fog computing refers to
extending cloud computing and its services to the edge of the
network. Fog computing is a decentralized infrastructure for
analysis of data and computing and can be used to store and
process time-sensitive data efficiently and quickly. Its main
goal is to enhance security, prevent data thefts, minimize the
data stored on the cloud and to increase the overall efficiency
of IoT applications. The latency in fog computation is less
than cloud computation because the fog layer is placed much
closer to the devices than the cloud. Only the selected and
important data is sent to the cloud for long-term storage.
Fog computing applications include smart vehicles, smart
homes, smart agriculture, health-care, smart traffic lights,
smart retail, software-defined networks, etc. Sending the
immense amount of data generated by IoT devices to the
cloud for processing and analyzing would be costly and
time-consuming. Along with minimizing network bandwidth
requirements, fog computing also reduces the frequency of
two-way communication between IoT devices and the cloud
[125].
In fog architecture, the data is collected at devices called
fog nodes which can analyze 40 percent of it [126]. It offloads
traffic from the core network minimizing the latency of IoT
devices. A fog node can be any device like a router, switch,
or a video surveillance camera which has computing, storage,
and network connectivity. These fog nodes can be installed
anywhere like on a factory floor or in a vehicle, provided it
has a network connection. Data is directed to the fog node,
aggregation node or cloud based on its time-sensitivity. Fog
nodes make the communication in IoT application secure by
providing cryptographic computations. Mere sensors and IoT
devices do not always have the necessary inbuilt resources for
that purpose [127].
D. SOLUTIONS PROVIDED BY FOG COMPUTING TO
OVERCOME IOT SECURITY THREATS
In regard to the attacks discussed in Section III, the solution
that fog computing provides or can provide to overcome
those security threats are discussed below.
1. Man-in-the-middle attack: Fog acts as a security
layer between end-user and cloud or IoT system. All
threats or attacks on the IoT systems need to pass
through the fog layer in between, and this layer can
identify and mitigate unusual activities before they are
passed to the system.
2. Data transit attacks: Data storage and management
is much better if performed on the secure fog nodes,
as compared to the IoT devices. Data will be better
protected if it is stored on the fog nodes as compared
to storing the data on the end-user devices. Fog nodes
also help in making the user data more available.
3. Eavesdropping: Using fog nodes, the communication
takes place between the end-user and the fog node
only, rather than routing the information through the
entire network. The chances of an adversary trying
to eavesdrop reduces a lot because the traffic on the
network is reduced.
4. Resource-constraint issues: Most of the IoT devices
are resource constrained and the attackers take advan-
tage of this fact. They try to damage the edge devices
and use them as the weak links to enter the system. Fog
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TABLE 5: Characteristics and Solutions provided by Fog Computing
Characterstics Solutions Provided By Fog
Decentralization
Verifiable computation [128]–[130]
Server-aided exchange [131], [132]
Big data analytics [133]–[137]
Data dissemination
Designing protocol [138]
Sharing data securely [139]–[143]
Searching data securely [144], [145]
Real-time services
Identity recognition [146]–[151]
Access management [152], [153]
Intrusion detection [154]–[159]
Transient storage
Recovery from attacks [160]
Data distribution [161]
Identifying and protecting sensitive data [162]
nodes can support the edge devices and can prevent
them from being affected by such attacks. A nearby
fog node can perform the more sophisticated security
functions necessary for protection.
5. Incident response services: Fog nodes can be pro-
grammed to provide real-time incident response ser-
vices. Fog nodes can generate a flag to the IoT system
or the end users as soon as they encounter a suspicious
data or request. Fog computing allows for malware
detection and problem resolution in transit. In many
critical appliations, it might not be possible to stop the
entire system to resolve malware incidences. Fog nodes
can help in such resolutions while the system is up and
running.
E. SECURITY CHALLENGES AND SOLUTIONS IN FOG
LAYER
Although fog layer provides various features and security
aspects for IoT applications, the movement of data and
computation to fog layer creates new vulnerabilities [120].
Therefore, before implementing fog-assisted IoT applica-
tions, these security and privacy goals of fog computing
are required to be studied. In this section, various features
provided by fog layer, privacy and security challenges faced,
and proposed solutions to overcome them are discussed.
Table 5 summarizes these issues and proposed solutions.
1. Real-Time Services: Fog computing tends to provide
a near real-time service in the IoT systems by perform-
ing computation near the data generation points.
• Intrusion detection: Policy violations and mali-
cious activities on fog nodes and IoT devices will
not be discovered if no proper intrusion detection
mechanism is implemented. The attacks might not
impact the whole architecture of fog computing,
but the attacker can control the local services.
Attacks targeting local services can be detected
by fog nodes by collaborating with their adja-
cent nodes. By observing program behavior and
host file systems, the atack on the cloud can be
detected [163].
• Identity authentication: There are various entities
involved in the process of offering and accessing
real-time services like fog nodes, service providers
and users. Trusting all the entities involved is an
arduous task, and creates security challenges for
IoT services and user’s data. Accessibility of ser-
vices should be given only to authentic and cred-
ible users; otherwise, attackers may compromise
the server and exploit services and user privacy.
Therefore, to prevent attackers from illegitimately
accessing services, identity authentication mech-
anisms are needed. To provide secure services,
some efficient identity authentication mechanisms
have been proposed in the past [164]–[169].
2. Transient Storage: Users can store and maintain
their data on fog nodes temporarily with the help of
transient storage. On the one hand, it helps in managing
data easily on local storage, but on the other hand, it
creates new challenges and security issues, especially
for maintaining data privacy.
• Identifying and protecting sensitive data: Data
stored in IoT devices may include social events,
traffic conditions, personal activities, temperature
and so on. Some of the data might be personal
or sensitive while some data may be made public.
Furthermore, for different users, the same data has
different security levels. Therefore, it is important
to identify and protect the sensitive data from the
large volume of information.
• Sharing data securely: To provide security, data
uploaded on fog nodes is first encrypted. No one
other than its owner can read that data once it is
encrypted. This creates a problem for data shar-
ing. To overcome this challenge, some crypto-
graphic techniques such as key-aggregate encryp-
tion, proxy re-encryption, and attribute-sharing,
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have been proposed in [170].
3. Data Dissemination: The data cannot be transferred
to the fog node without encryption, due to security
issues. Due to this movement of encrypted data to the
fog node, many desirable features are sacrificed such
as sharing, searching, and aggregation.
• Searching data securely: As discussed in transient
storage, data is encrypted before uploading. How-
ever, once it is encrypted, searching or retrieving
on the ciphertext becomes difficult for owners
as well as other entities. In order to retrieve the
information from encrypted text, search-able en-
cryption and its privacy levels are defined in [171].
A dynamic symmetric search-able scheme is intro-
duced in [172].
• Data aggregation: Fog nodes might need to aggre-
gate the data in certain cases to prevent data leak-
age and reduce communication overhead. It is im-
portant to develop secure aggregation algorithms
to prevent data thefts. Various homomorphic en-
cryption schemes, such as BGN encryption [173]
and Paillier encryption [174], have been proposed
to achieve secure data aggregation.
4. Decentralized Computation: The data stored on the
fog nodes can be processed and analyzed for better re-
sults. However, such computations have several threats
and risks associated with them. For example, attackers
can not only control the analyzed results, but can also
expose processed data.
• Server-aided computation: Tasks which cannot be
executed by IoT devices themselves are com-
puted with the help of fog nodes. However, this
can lead to exposure of data to attackers, if the
fog nodes which received data from IoT are al-
ready compromised. Server-aided computation is
one such method whose aim is to provide secure
computation [175].
• Verifiable computation: Users rely on the fog
nodes to compute their data. There must be a se-
cure mechanism to verify the computation results
coming from the fog node. Authors in [176], [177]
have proposed certain multi-user mechanisms that
help with verifiable computation.
VII. IOT SECURITY USING MACHINE LEARNING
The area of machine learning (ML) has attracted significant
interest over recent years. Many domains are using ML for
their development, and it is being used for IoT security
as well. ML appears to be a promising solution to protect
IoT devices against cyber attacks by providing a different
approach for defending against attacks as compared to other
traditional methods.
A. SOLUTIONS PROVIDED BY ML TO OVERCOME
SECURITY THREATS
In regard to the attacks discussed in Section III, the solutions
provided by ML to overcome these security threats are dis-
cussed below.
1. DoS Attack: DoS attacks on IoT devices or origi-
nating from IoT devices are a serious concern. One
approach to prevent such attacks is to use a Multi-
Layer Perceptron (MLP) based protocol that secures
networks against DoS attacks [178]. The authors of
[179] have proposed a particle swarm optimization and
back propagation algorithm to train a MLP that helps
in enhancing the security of wireless networks. ML
techniques help in increasing the deduction accuracy
and securing IoT devices vulnerable to DoS attacks.
2. Eavesdropping: Attackers may eavesdrop on mes-
sages during data transmission. To provide protection
from such attacks, ML techniques such as Q-learning
based offloading strategy [180] or non-parametric
Bayesian techniques [181] can be used. Schemes such
as Q-learning and Dyna-Q are ML techniques that may
also be used to protect devices from eavesdropping.
Evaluation of these schemes via experiments and re-
inforcement learning is presented in [182].
3. Spoofing: Attacks from spoofers can be avoided by
using Q-learning [182], Dyna-Q [182], Support Vector
Machines (SVM) [183], Deep Neural Network (DNN)
model [184], incremental aggregated gradient (IAG)
[46], and distributed FrankWolfe (dFW) [185] tech-
niques. These techniques not only increase the detec-
tion accuracy and classification accuracy but also help
in reducing the average error rate and false alarm rate.
4. Privacy Leakage: Collection of personal information
such as health data, location, or photos puts the user’s
privacy at stake. Privacy-preserving scientic computa-
tions (PPSC) [186] should be employed for preventing
privacy leakage. A commodity integrity detection algo-
rithm (CIDA) which is based on the Chinese remainder
theorem (CRT) is another technique that has been
proposed to develop IoT application trust [187].
5. Digital Fingerprinting: Digital fingerprinting is one
of the upcoming and promising solutions to secure
IoT systems and to help the end users gain sufficient
trust in the applications. Fingerprints are being widely
used to unlock smartphones, approve payments, unlock
the car and home doors, etc. Due to its low cost,
reliability, acceptability and high-security level, digital
fingerprinting is emerging as a dominant bio-metric
identification method [188]. Apart from the benefits of
digital fingerprinting, there are various challenges to
efficiently use this technique in IoT, such as fingerprint
classification, image enhancement, feature matching,
etc. Various machine learning based algorithms have
been developed to provide some non-traditional so-
lutions to overcome these challenges [189], some of
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which are discussed below.
• Support Vector Machine: SVM is a training al-
gorithm for non-linear and linear classifications,
principal component analysis, text categorization,
speaker identification, and regression. It maxi-
mizes the gap between the decision boundary and
training patterns. Authors of [190] have discussed
the use of SVM in digital fingerprinting in detail.
They have also compared it with other traditional
models. A feature vector is built based on pixel
values of the fingerprint, and it is used to train the
SVM. Various patterns behind the fingerprint are
analyzed, and then the matching of a fingerprint is
done based on patterns identified.
• Artificial Neural Networks: ANN is one of the
most commonly used algorithms in the machine
learning. It offers many advantages like fault
tolerance, adaptive learning, and generalization.
In [191] a framework has been proposed for using
ANN to identify fingerprints digitally. The digital
values of various features in the fingerprint like
minutiae, ridge ending, and bifurcation is applied
as the input to the neural network for training pur-
pose using back propagation algorithm of ANN.
The verification of the fingerprint is done based
on the previous experiential values stored in the
database.
The fundamental need in IoT is to secure all the systems
and devices that are connected to the network. The role of
ML is to use and train algorithms to detect anomalies in IoT
devices or to detect any unwanted activity taking place in IoT
system to prevent data loss or other issues. Therefore, ML
provides a promising platform to overcome the difficulties
faced in securing IoT devices. Further contributions in this
field are required to maintain the growth of IoT.
VIII. IOT SECURITY USING EDGE COMPUTING
Edge and fog computing are both extensions of cloud com-
puting which is widely used by various organizations. Cloud,
fog and edge may appear similar but they constitute different
layers of IoT applications. The main difference between
cloud, fog and edge computing is the location of intelligence
and power computation. The cloud is deployed at a much
larger scale that needs to process huge amount of data and is
situated at comparatively more distance from its users [192].
To overcome the problems faced by cloud computing, edge
computing is used as a solution where a small edge server is
placed between the user and the cloud/fog. Some processing
activity is performed at the edge server, rather than the cloud.
Edge computing architecture consists of edge devices, cloud
server and fog nodes as shown in Figure 8 [193].
In an edge computing framework the computation and
analysis power is provided at the edge itself. The devices
in an application can create a network among themselves
and can cooperate among each other to compute the data
[63]. Consequently, a lot of data can be saved from going
FIGURE 8: Edge Computing Architecture.
outside the device, either to cloud or to fog nodes, and
this can enhance the security of the IoT application. Edge
computing also helps in providing low communication cost
by preventing the need of moving all the data to the cloud
[66].
A. USING EDGE COMPUTING TO SECURE AND
IMPROVE IOT
In regard to the attacks discussed in Section III, the solutions
that edge computing provides or can provide to overcome
these security threats are discussed below.
1. Data Breaches: In edge computing, all the data is
stored and processed within the device or local net-
work. There is no movement of the data from the data
originator to the processor. This prevents the data from
being in transit and thereby prevents the risk of data
thefts and data breaches. In fog computing there is
some movement of data from a device to fog layer and
adversaries can take advantage of this movement [194].
2. Data Compliance Issues: Many countries have strict
regulatory acts to prevent data movement outside their
boundaries, e.g., European Union’s GDPR (General
Data Protection Regulation). Using edge computing,
organizations can keep the data within their bor-
ders and ensure compliance with data sovereignity
laws [195].
3. Safety Issues: With the increase in the deployment of
cyber-physical systems, security and safety are consid-
ered as integral issues. If there is even a little delay in
responses, then that may lead to physical safety issues.
For example, if the sensors in a car predict that a crash
is about to happen, then the airbags have to be deployed
immediately. If the sensors completely rely on sending
all the data to the cloud and waiting for the response
from the cloud to perform any action, then that may be
too late to prevent injuries or loss of life. Surveillance
cameras can also be empowered using edge computing
and they can themselves analyze the anomalies and can
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send the summarized and suspected data to the data
centers to achieve faster response times.
4. Bandwidth Issues: IoT application generate a lot of
data at very high rate. Most of this data is raw and
of relatively low-value. Sending all the data to the
cloud involves a lot of bandwidth cost as well, along
with the security challenges of data movement. If edge
computing is used, then a lot of data cleaning and
aggregation can be done at the edge nodes and only
the summarized data, if required, needs to be sent to
the cloud [196].
B. CHALLENGES IN EDGE LAYER
Although edge computing provides various features to in-
crease the security and performance of IoT applications, there
are various challenges associate with completely relying on
the edge layer for all computation. Edge devices include sen-
sors, RFID devices, actuators, tags, and embedded devices.
The edge layer is highly susceptible to attacks in an IoT
system. If the edge layer is compromised, then the entire
system may be compromised. MQTT and COAP are the most
popular protocols for the edge layer. Both these protocols do
not use any security layer by default. Although the option to
add an optional security layer in the form of TLS for MQTT
and DTLS for COAP is present, it creates additional overhead
in terms of processing and bandwidth. Issues specific to edge
devices include sleep deprivation attacks, battery draining at-
tacks, and outage attacks. Edge devices are typically resource
constrained, and the most important resource they rely upon
is the battery backup. The foremost and easiest way to attack
the edge devices is to somehow deplete the battery of an edge
device. For example, an attacker might force the edge device
to do some power hungry or infinite loop computation [197].
The process of striking a balance between storing and
processing data on edge or cloud is very important. Keeping
too much data on edge may also lead to overwhelming of the
edge devices and may impact the entire application.
IX. OPEN ISSUES, CHALLENGES, AND FUTURE
RESEARCH DIRECTIONS
There are some performance and security issues in the use
of blockchain, fog computing, edge computing and machine
learning for IoT security that are yet to be solved. This section
discusses some of these issues.
The security of blockchain depends on its method of
implementation and the use of software and hardware in that
implementation. Since all the transactions made by users
in blockchain are public, there is a possibility that private
information of users can be revealed. Also, as the number
of miners increases, the size of blockchain also increases
continuously. This increases the cost of storage and reduces
the speed of distribution over the whole network, giving rise
to issues like scalability and availability of blockchain [198].
Since fog computing is a nontrivial extension of cloud
computing, some of the issues such as security and privacy
will continue to persist [120]. Therefore, before implement-
ing fog-assisted IoT applications, these security and privacy
goals of fog computing are required to be studied. Some of
the challenges and research issues on security and privacy in
IoT environments and the solutions provided by fog comput-
ing are discussed in [127].
There are many machine learning algorithms in existence.
Therefore, it is imperative to select a proper algorithm suit-
able for the application. Selecting a wrong algorithm would
result in producing “garbage” output and will lead to loss
of effort, effectiveness and accuracy. Similarly, choosing
the wrong data set will lead to “garbage” input producing
incorrect results. The success of a machine learning solution
depends on these factors as well as diversity in selecting data.
If the data is not clustered and classified, the prediction accu-
racy will be lower. Also, the historical data may contain many
ambiguous values, outliers, missing values, and meaningless
data. IoT applications are creating a huge amount of data,
and therefore it is a difficult task to clean and preprocess that
data accurately. Various features like attribute creation, linear
regression, multiple regression, removing redundancies and
compressing data are required to effectively use machine
learning for securing the IoT.
In case of edge computing, data security and user privacy
are the main concerns. An user’s private data can be leaked
and misused if a house that is deployed with IoT devices is
subjected to cyber attacks. For example, a person’s presence
or absence at home can be revealed simply by observing the
electricity or water usage data. Since the data is computed
at the edge of data resource (e.g., home), therefore, the user
has to be aware of some of the measures like securing WiFi
connections. Secondly, data at edge should be owned fully by
the user, and he/she should have control on which data to be
shared.
Some of the future research directions in this field are:
• The edge devices are most resource constrained devices
in the IoT and are therefore uniquely vulnerable to
attacks. Penetration studies show that while it takes very
little power to implement best practice security for the
edge nodes, they are still highly vulnerable to a variety
of malicious attacks.
• The gateways between different layers in the IoT system
need to be secured. Gateways provide an easy entry
point for the attackers into the IoT system. End to end
encryption, rather than specific encryption techniques
for specific protocols would be a promising solution to
secure the data passing through the gateways. The data
should be decrypted only at the intended destination and
not at the gateways for protocol translation.
• Inter-fog sharing of resources is one of the areas where
further work needs to be done. As of now, when the
fog layer is unable to process the requests due to heavy
load, the requests are forwarded to the cloud. There can
be resource sharing between neighboring fog layers to
prevent unwanted requests to be transferred to the cloud.
• The current blockchain architecture is highly limited in
terms of the number of nodes in permissioned networks
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and in terms of throughput in permissionless networks.
Various consensus algorithms are being designed to
support high throughput along with a large number of
nodes or users.
• Fog layer can be made more intelligent using various
ML and AI techniques. Fog layer must be able to decide
the duration for which the data in the fog should be
retained and when the data should be discarded or
shifted to the cloud for prolonged storage.
• More efficient and reliable consensus mechanisms can
be designed to reach consensus among the nodes along
with preventing rampant use of computation power.
The current consensus algorithms are highly resource
hungry and less efficient.
• The tamper-proof feature of blockchain is ending up
into a collection of a lot of garbage data and addresses.
There is a lot of invalid data that is never deleted like
the addresses of the destructed smart contracts. This
affects the performance of the overall application and
better ways need to be designed to efficiently handle the
garbage data in the blockchain.
• Data analysis in near real-time and in the proximity
of the IoT node is crucial for successful deployment
of IoT applications. Various ML-based algorithms can
be designed to analyze the data in the node itself to
prevent the data transit for analysis. This can further
enhance the security of the application by preventing
data movement.
X. CONCLUSION
In this survey, we have presented various security threats
at different layers of an IoT application. We have covered
the issues related to the sensing layer, network layer, mid-
dleware layer, gateways, and application layer. We have
also discussed the existing and upcoming solutions to IoT
security threats including blockchain, fog computing, edge
computing, and machine learning. Various open issues and
issues that originate from the solution itself have also been
discussed. The state-of-the-art of IoT security has also been
discussed with some of the future research directions to
enhance the security levels is IoT. This survey is expected
to serve as a valuable resource for security enhancement for
upcoming IoT applications.
ACKNOWLEDGMENT
This research was supported by the National Research Foun-
dation, Prime Minister’s Office, Singapore under its Corpo-
rate Laboratory@University Scheme, National University of
Singapore, and Singapore Telecommunications Ltd
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10.1109/ACCESS.2019.2924045, IEEE Access
Vikas Hassija et al.: A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures
VIKAS HASSIJA is currently an Assistant Pro-
fessor at Jaypee Institute of Information and tech-
nology (JIIT) , Noida. He received his B.tech de-
gree from M.D.U University, Rohtak, India, 2010
and M.S. degree in Telecommunications and Soft-
ware engineering from Birla Institute of Technol-
ogy and Science (BITS), Pilani, India in 2014.
He is currently pursuing Phd from JIIT in IoT
security and blockchain. He has 8 years of industry
experience and has worked with various telecom-
munication companies like Tech Mahindra and Accenture. His research
interests include IoT security, Network security, Blockchain and distributed
computing.
VINAY CHAMOLA received his B.E. degree in
electrical electronics engineeerig and Master’s
degree in communication engineering from Birla
Institute of Technology Science (BITS), Pilani,
India in 2010 and 2013 respectively. He received
his Ph.D. degree in electrical and computer en-
gineering from the National University of Singa-
pore, Singapore, in 2016. From June to Aug. 2015,
he was a visiting researcher at the Autonomous
Networks Research Group (ANRG) at University
of Southern California (USC), USA. Currently he is a Research Fellow at
the National University of Singapore. His research interests include solar
powered cellular networks, energy efficiency in cellular networks, internet
of things, and networking issues in cyberphysical systems.
VIKAS SAXENA is currently Professor and head
of computer science and information technology
department at Jaypee Institute of Information and
Technology, Noida, India. He received his B.tech
degree from IET, MJP Rohilkhand University,
Breilly, india in 2000, the M.E degree from VJTI,
Mumbai, India, in 2002, and the Ph.D. degree
in CSE from Jaypee Institute of Information and
Technology, in 2009. He is having more than 17
years of experience of teaching and research. His
research interests include image processing, blockchain, computer vision,
software engineering and multimedia. Dr. Vikas served as Publicity Co-
Chair in the International Conference IC3-2008, India conducted by JIIT
and University of Florida, USA.
DIVYANSH JAIN is currently working as a sum-
mer intern at Birla Institute of Technology and Sci-
ence (BITS), Pilani and is expecting B.tech degree
from Jaypee Institute of Information and Tech-
nology, in 2020. He has completed few projects
on deep learning, blockchain and artificial intel-
ligence. His areas of interest include distributed
algorithms, and data structures.
PRANAV GOYALis currently working as a sum-
mer intern at Birla Institute of Technology and Sci-
ence (BITS), Pilani and is expecting B.tech degree
from Jaypee Institute of Information and Technol-
ogy, in 2020. He has completed few projects on
blockchain applications and machine leaning. His
areas of interest include distributed computing,
IoT and data analytics.
BIPLAB SIKDAR [S’98, M’02, SM’09] received
the B.Tech. degree in electronics and communica-
tion engineering from North Eastern Hill Univer-
sity, Shillong, India, in 1996, the M.Tech. degree
in electrical engineering from the Indian Institute
of Technology, Kanpur, India, in 1998, and the
Ph.D. degree in electrical engineering from the
Rensselaer Polytechnic Institute, Troy, NY, USA,
in 2001. He is currently an Associate Professor
with the Department of Electrical and Computer
Engineering, National University of Singapore, Singapore. His research
interests include wireless MAC protocols, transport protocols, network
security, and queuing theory.
24 VOLUME x, 2019