Cyber Security Risks in Cloud Computing

This assignment delves into the multifaceted risks associated with cybersecurity in cloud computing environments. It explores various types of threats, vulnerabilities, and attack vectors specific to cloud platforms. Furthermore, the analysis considers existing security measures, best practices, and emerging technologies aimed at mitigating these risks.

Running head: RISK MANAGEMENT
Risk Management
Name of the Student
Name of the University
Author’s Note

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Table of Contents
Introduction..........................................................................................................................3
1. Overview and ENISA big data Infrastructure.................................................................3
1.1 Overview....................................................................................................................3
2. Top Threats associated with ENISA and their significance............................................6
2.1. Most significant Threat.............................................................................................8
3. Threat agents, impact and threat probability...................................................................8
3.1. Minimization of the Impact of the Threat...............................................................10
3.2. Threat and Probability trends..................................................................................11
4. Improving ETL process.................................................................................................11
5. Current State of IT security...........................................................................................11
Conclusion.........................................................................................................................12
Reference...........................................................................................................................13

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Introduction
The case study mainly deals with the threats and the security issues associated with big
data. Big data mainly deals with the storage of large amount of data that can be accessed by
public to be used in business purposes. The use of big data can offer significant benefits to the
business and organizations (Wu et al., 2014). However, there are different security issues
associated with the use and access of big data, which ultimately results in data breach and loss of
data. This case study deals with the threats associated with ENISA and the key threat agents
involved in this. The report discusses the process of elimination of threat from the system.
Furthermore, it discusses the different security issues associated with ENISA and the key threat
agents along with the threat infrastructure. The impact of these threat and the threat mitigation
process is discussed in this report (Inukollu, Arsi & Ravuri, 2014). The detailed analysis of
different aspects of the case study is elaborated in the following paragraphs.
1. Overview and ENISA big data Infrastructure
A brief overview of the ENISA case study and the infrastructure diagram of ENISA is
elaborated and illustrated in the following paragraphs-
1.1 Overview
The case study focuses on the big data, its increasing implementation and use along with
the threats associated with it. The increasing data and security breaches is an alarming issue in
today’s world and therefore proper measures are to be taken in order to curb it (Wright & De
Hert, 2012). The case study elaborates the threats associated with the technology of big data,
which has a significant role in affecting the various aspects of the society. The impact of big data
is huge in the thriving data driven economy. Big data has increasing use in different fields such

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as in military applications, fighting terrorism and also in research work ( ENISA 2017).
Therefore, this technology offers numerous advantages and is considered as a major source of
information. However, this source of information is exposed to different threats and attacks by
different threat agents that are discusses in the report. The big data systems can be widely used in
different applications. The case study aims at deepening the understanding the threats and
recommendations of eliminating the threats. The case study further aims at suggesting different
risk management strategy for eliminating the risks and threats associated with big data. The case
focuses on the use of cloud storage as the major data storage system of big data. Cloud storage is
however associated with different types of risks and security issues that need to be considered in
order to eliminate the risk of data breach and data loss. The case study report also elaborates the
big data architecture, which is a high-level conceptual model that that demands certain security
requirements in Big Data. The different layer of big data infrastructure consists of different data
sources, data storage, computing models and presentation (Gonzalez et al., 2012). The big data
asset taxonomy elaborated in the case study gives an overview of the big data assets and
structure. The major component of big data assets includes big data analytics, security and
privacy techniques. The different threat identified in the case study is mapped in the big data
asset. The infrastructure diagram of ENISA along with the threat associated with it is illustrated
below.

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Figure 1: Representing the ENISA big data security Infrastructure
(Source: created by author using MS Visio)
2. Top Threats associated with ENISA and their significance
The storage of huge amount of data is associated with different security threats. Attackers
plan and implement these attacks in order to gain access over voluminous amount of data. the top
threats associated with ENISA are listed below ( ENISA, 2017)-
1) Leak of data due to the use of unsecure APIs: Big data is built with very little security
and data breaches due to unsecure APIs are very common. Different injection attacks can be
launched with by making use of unsecure APIs and therefore this is considered as a mojor threat
in big data.
The assets that are mainly targeted by this threat include data, big data analytics, software
and computing models.
2) Inadequate or improper designing of the security system may lead to arrival of number
of threats. The techniques used in fusion of heterogeneous data sources increases the redundancy
in data representation and therefore, managing the data becomes impossible. This redundancy in
data increases the probability of data disclosure and data leaks as the managing of data becomes
impossible (Theoharidou et al., 2013).
The assets that are mainly targeted by this threat include data and applications.
3) Denial of service attack: Denial of service attack mainly aims are making the resources
unavailable for the authorized users. This is implemented by exploiting the vulnerabilities

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associated with the system and as a result of DOS, the performance of the system also decreases
(Tan et al., 2014).
The assets that are targeted by this attack include servers and networks.
4) Malicious code and software activity: The most significant threat associated with big
data is mainly due to the malicious code and software activity. The different threat agents of this
threat include ransomware, Trojan horses, works, trapdoors, spoofing and viruses. These threat
agents are infused into the system with the help of a malicious codes and programs
(Theoharidou, Tsalis & Gritzalis, 2013). After the threat is installed in the system, the attacker
gains access of the entire system and therefore, the risk associated with this particular threat is
very high. These threats easily spread from system to system and therefore it must be eliminated
with highest priority (Seshardi et al., 2012).
The assets that are mainly targeted by this threat consists of database and computing
infrastructure models.
5) Use of rogue certificates: generation of rouge certificate in order to gain access to
certain devices is a significant threat in big data. this can result in data theft, data manipulation,
data leakage and the misuse of data (Pearson, 2013).
The assets that are mainly targeted by this particular threat includes software, hardware
and associated data.
6) Interception of Information: The attackers can intercept the transfer of data among the
different nodes mainly by making use of communication links is a prominent threat.

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The assets that are mainly targeted by this threat includes data, application and back end
services.
7) Identity fraud: Accessing the data impersonating someone else gives rise to the threat
of identity fraud. This is a significant issue because the it mainly deals with the loss of personal
information (Roberts, Indermaur & Spiranovic, 2013).
The assets that are targeted by this threat include personal identifiable information and
back end services and servers.
2.1. Most significant Threat
The top threats that are discussed above results in a considerable data security risks in big
data. Out of them, the threat due to malicious program and activities is most significant. This is
because this type of threat can easily spread from system to system and with the installation of
the malicious code, the attacker can gain access to the whole system. Hacking is one of the major
source of injecting malicious code into the system (Chen & Zhao, 2012). This is most significant
because the attacker after gaining the access to the system can easily modify and manipulate the
data. The attacker can make use of those data for personal benefits thus giving rise to significant
threat and data leakage. This threat or risk should be eliminated from the system with immediate
concern in order to eliminate the risks associated with the big data. Implementing a proper
intrusion detection system can further help in eliminating the risks of hacking (Pavlyushchik,
2014).
3. Threat agents, impact and threat probability

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The top threat agents, the impact of the threats associated with big data and the threat
probability of ENISA are elaborated is listed below-
1) Corporation: One of the major threat agents associated with the security concerns of
big data is the corporation or organizations that use ill techniques of data manipulation and
stealing of data in order to gain competitive advantages.
2) Cyber criminals: This is one of the most dangerous threat agent associated with the
privacy issue of big data. The cyber criminals gain access of the big data by different techniques
mainly for financial benefits. They can intrude into the system with an intention of data stealing
and therefore proper risk management strategies are to be implemented to secure these data.
3) Cyber terrorists: The cyber terrorists are similar to cyber criminals but the effects of
their attack are wide spread. The main target of cyber terrorists are critical infrastructures and
large organizations. Cyber terrorist mainly target these organization as any impact or effect over
these organization can cause severe impact over society as well (Taylor, Fritsch & Liederbach,
2014).
4) Script kiddies: These threat agents are not very dangerous as they make use of already
developed codes and programs in order to launch an attack. Therefore, the effect of these attacks
are very negligible and the risk associated with this threat agents can be easily avoided.
5) Hacktivists or online social hackers: These threat agents mainly target hig profile
website to promote their views. Computer systems are used in order to launch and execute an
attack.

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6) Employees: Employees of an organization can be a major threat agent as well. This is
because they possess a good knowledge of the data and security system of an organization and
make use of that knowledge to launch an attack. Data manipulation cannot be a hard task for an
employee of an organization and therefore they are considered as a major threat agents.
7) Nation States: These are most dangerous threat agents out of the discussed threat
agents. Nation states are sophisticated cyber criminals and are associated with launching a well
planned attack using the modern tools and techniques. This attacker have high level skill and
expertise and therefore considered as a significant threat agent.
3.1. Minimization of the Impact of the Threat
Minimizing the impact of the threat is essential in order to eliminate the security and the
privacy issues associated with the system. The different measures that can be undertaken in order
to eliminate the risk are listed below-
1) Access control is an important aspect of data protection by preventing the
unauthorized access of data. Since the storage of big data involves storage of data in cloud, it is
vulnerable to a number of attacks and therefore access control may considerably help in data
protection (Brucker et al., 2012)
2) Limiting the use of data using modern cryptographic techniques and proper encryption
is another suggested method of data protection (Stallings & Tahiliani, 2014).
3) Implementing better and effective security systems is essential for preventing the
intrusion into the system.

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4) Training the staffs and users of an organization in order to generate awareness among
the employees about need of information security is essential for data protection.
3.2. Threat and Probability trends
The threat associated with the use and access of big data in increasing considerably as the
attackers are coming up with different ways of implementing an attack into the system. The
threat probability is needed to be reduces in order to secure the big data. different security
measures can be implemented in order to protect this data. the threats are becoming more
dangerous and hence, curing it from the roots becomes essential.
4. Improving ETL process
ENISA threat landscape or ETL reports about the different threats associated with an
organization. The report mainly deals with the threats associated with the information and
communication technology asset (ENISA, 2017). The major drawback of this ETL is that it only
discusses the threat associated with the big data. The threats and the threat agents have evolved
with time and therefore the report should contain a more detailed structure of the threats and the
consequences. The EYL can be improvised by incorporating a detailed overview of the threats
associated with the big data and its use (Cherdantseva et al,. 2016).
5. Current State of IT security
The ENISA organization is not satisfied with the current IT structure of the organization
as there are number of threats associated with the security system. The security essentials are
needed to be updated and stronger security features are to be incorporated into the statement.
With the increase of a number of threats and their sophistication, a stronger security

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infrastructure is essential. The report discusses the number of different security measures that can
be implemented (Von Solms & Van Niekerk, 2013). The major drawback of the current security
system is that it cannot filter the redundancy of data which gives rise to a number of threats.
Different risk management strategies can be implemented to eliminate the risks associated with
the IT security system of ENISA. The use of insecure APIs can be avoided in order to eliminate
the risk of intrusion into the system. Furthermore, proper intrusion detection system and firewall
can be implemented in order to protect the data (Albakri et al., 2014).
Conclusion
Therefore, from the above discussion it can be concluded that the IT security structure of
ENISA should undergo improvisation. The report identifies the major threats associated with the
system and the threat agents. The report further suggests the different techniques to minimize the
risks associated with the system. The major threats agents are identified in the report that are
responsible for implementing an attack. Big Data has increasing use in today’s world and
therefore the security essentials of big data should be thoroughly improvised in order to protect
the data.

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Reference
Albakri, S. H., Shanmugam, B., Samy, G. N., Idris, N. B., & Ahmed, A. (2014). Security risk
assessment framework for cloud computing environments. Security and Communication
Networks, 7(11), 2114-2124.
Big Data Threat Landscape — ENISA. (2017). Enisa.europa.eu. Retrieved 6 September 2017,
from https://www.enisa.europa.eu/publications/bigdata-threat-landscape
Brucker, A. D., Hang, I., Lückemeyer, G., & Ruparel, R. (2012, June). SecureBPMN: Modeling
and enforcing access control requirements in business processes. In Proceedings of the
17th ACM symposium on Access Control Models and Technologies (pp. 123-126).
ACM.
Chen, D., & Zhao, H. (2012, March). Data security and privacy protection issues in cloud
computing. In Computer Science and Electronics Engineering (ICCSEE), 2012
International Conference on (Vol. 1, pp. 647-651). IEEE.
Cherdantseva, Y., Burnap, P., Blyth, A., Eden, P., Jones, K., Soulsby, H., & Stoddart, K. (2016).
A review of cyber security risk assessment methods for SCADA systems. computers &
security, 56, 1-27.
Gonzalez, N., Miers, C., Redigolo, F., Simplicio, M., Carvalho, T., Näslund, M., & Pourzandi,
M. (2012). A quantitative analysis of current security concerns and solutions for cloud
computing. Journal of Cloud Computing: Advances, Systems and Applications, 1(1), 11.
Inukollu, V. N., Arsi, S., & Ravuri, S. R. (2014). Security issues associated with big data in
cloud computing. International Journal of Network Security & Its Applications, 6(3), 45.

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Pavlyushchik, M. A. (2014). U.S. Patent No. 8,713,631. Washington, DC: U.S. Patent and
Trademark Office.
Pearson, S. (2013). Privacy, security and trust in cloud computing. In Privacy and Security for
Cloud Computing (pp. 3-42). Springer London.
Roberts, L. D., Indermaur, D., & Spiranovic, C. (2013). Fear of cyber-identity theft and related
fraudulent activity. Psychiatry, Psychology and Law, 20(3), 315-328.
Seshardi, V., Ramzan, Z., Satish, S., & Kalle, C. (2012). U.S. Patent No. 8,266,698. Washington,
DC: U.S. Patent and Trademark Office.
Stallings, W., & Tahiliani, M. P. (2014). Cryptography and network security: principles and
practice (Vol. 6). London: Pearson.
Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2014). A system for denial-of-service
attack detection based on multivariate correlation analysis. IEEE transactions on parallel
and distributed systems, 25(2), 447-456.
Taylor, R. W., Fritsch, E. J., & Liederbach, J. (2014). Digital crime and digital terrorism.
Prentice Hall Press.
Theoharidou, M., Tsalis, N., & Gritzalis, D. (2013, June). In cloud we trust: Risk-Assessment-as-
a-Service. In IFIP International Conference on Trust Management (pp. 100-110).
Springer, Berlin, Heidelberg.
Von Solms, R., & Van Niekerk, J. (2013). From information security to cyber security.
computers & security, 38, 97-102.

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Wright, D., & De Hert, P. (2012). Introduction to privacy impact assessment. In Privacy Impact
Assessment (pp. 3-32). Springer Netherlands.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions
on knowledge and data engineering, 26(1), 97-107.