Risk Management: Biometric Methods, PETs, and WSN Threats

This study discusses three biometric methods, their application, advantages, and disadvantages, PETs like communication anonymizers, zero knowledge, and Iprivacy, and WSN threats like Sybil, wormhole, and DoS attacks. Mitigation strategies are also discussed.

Running Head: RISK MANAGEMENT 1
Risk Management
Student’s Name
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Table of Contents
Introduction...............................................................................................................................................3
Biometric recognition................................................................................................................................3
Fingerprint.............................................................................................................................................3
Fingerprint technique and technology.............................................................................................3
Application.............................................................................................................................................4
Hand Geometry.....................................................................................................................................5
Technique and Technology...............................................................................................................5
Application.............................................................................................................................................5
Iris Recognition......................................................................................................................................6
Technique and Technology...............................................................................................................6
Application.............................................................................................................................................7
Privacy-enhancing technologies (PETs)...............................................................................................7
Communication Anonymizers..............................................................................................................7
Zero Knowledge.....................................................................................................................................7
Iprivacy..................................................................................................................................................8
Wireless Sensors Network Threats and Vulnerabilities.........................................................................8
Sybil Attack............................................................................................................................................8
Recommendations on Mitigation of the Sybil Attack......................................................................9
Wormhole Attack..................................................................................................................................9
Recommendations on Mitigation of the Wormhole Attacks...........................................................9
Denial of Service Attack........................................................................................................................9
Recommendations on Mitigation of the DoS.................................................................................10
Conclusion................................................................................................................................................10
References................................................................................................................................................11

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Introduction
This study is going to look at three different biometric methods, their application as well as they
advantages and disadvantages. The second part will be touching on Privacy-enhancing
technologies (PETs) and the last part will be looking at the risks of Wireless sensor networks
(WSNs). These aspects are risk management factors when using the internet.
Biometric recognition
Biometric recognition is an authorization and authentication technology of identifying
people automatically using their distinct biological, personal, physiological and behavioral
characteristics. These characteristics include, iris, face, hand geometry, fingerprint and voice
(Bolle, Connell, Pankanti, Ratha & Senior, 2013; Jain, Nandakumar & Ross, 2016).
Fingerprint
A fingerprint is a unique pattern and feature (parallel ridges and furrows of same width)
of a finger and is used for identification and recognition. What recognizes fingerprints is not the
ridges and furrows but Minutia; features on the ridges (Abdolahi, Mohamadi & Jafari, 2013).
Fingerprint technique and technology
One of the techniques used in fingerprint biometrics includes that one of inked fingers.
This is simply pressing a finger against an inked surface then doing the same on a piece of paper
(Daluz, 2014). However in the last decade, a new technique has been developed to take
fingerprints without using ink (Bolle, et al., 2013). The ink-less method senses ridges on a finger
that is on the surface of the livescan fingerprint scanner. Then the livescan scanner has different
technologies of acquiring the livescan image like the frustrated total internal reflection and other
optical methods. Representation of the fingerprints may be at the client end of the application or

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the underdone image conveyed to a server for processing. The image is either compressed or
decompressed. The Wavelet Scalar Quantization (WSQ) is the recommended compression
technique by the FBI. Fingerprint matching techniques include image techniques especially when
the finger is small, feature techniques which extracts landmarks and develops different machine
representations of a fingerprint from these features, and the hybrid techniques that combine the
image’s and feature’s techniques or makes use of neural networks to increase accuracy (Bolle, et
al., 2013).
Advantages
First, fingerprint acquisition is easy; in form of impressions of inked fingers on paper and
straight impressions in things such as clay (Bolle, et al., 2013). The technology also is a cheap
and easy to use security system since a few security people are needed for identification. It is
possible to use fingerprints for continuous identification since because of its long history of use.
Fingerprints give a substantial body of real world data unlike voice and iris scanning
(Technology assessment using biometrics for border security, 2018).
Disadvantages
The first and very obvious disadvantage is the fact that a person may change physically
but the scanners don’t consider that. The installation of computer hardware and software
programs is expensive. There are incidents of false rejections or acceptance hence inaccuracy in
identification (Bolle, et al., 2013).
Application
The technology is applicable in business organizations in the developments of open
network and flexible migration of employees as a measure to protect against information
leakage.

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Hand Geometry
Hand geometry recognition has a long history from the 1980s (NSTC, 2018)
Technique and Technology
The technique used here is that one of determining and recording the length, width,
thickness and surface area of a person’s hand by a plate (Saito & Soliven, 2014). A camera is
used to capture an outline image. The hand is normally positioned on a plate with the palm
facing downwards. There are 5 fasteners that sense when the small hand is in position. A CDC
camera captures the top view of the hand to include the distance measurements (NSTC, 2018).
Advantages
The hand geometry system are easy to use, are accepted publicly because of its
association with common authorized access, and have easy integration capabilities despite their
use of special hardware. They also require small data and hence widely used in verification
(NSTC, 2018).
Disadvantages
The technology is not very unique and that is what that limits its applicability to verifying
tasks only. Again it is very expensive and invalid for an arthritic person who is unable to place
his or her hand on a scanner properly (NSTC, 2018).
Application
According to NSTC, the technology is applicable for physical access, attendance
tracking, and personal verification especially in security and accountability sectors (NSTC,
2018).

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Iris Recognition
According to FBI, Iris Recognition involves automatic scrutinizing the arbitrary outline
of the iris (muscle in the eye regulating the size of the pupil). The iris has different coloring
depending on the amount of melatonin pigment within it.
Technique and Technology
Iris recognition technique is based on its unique and structural distinction. The process is
automated through an algorithm (FBI, 2018). The iris is first sited by use of landmark features
and its distinct shape which allows for imaging, feature isolation as well as extraction. A high
quality digital camera is used for imaging. The modern one uses the infrared light to illuminate
the subject without harm or discomfort. A 2D Gabor wavelet filters charts the iris segments into
phasors or vectors. The pattern is described using an Iris Code by use of this phasor’s data.
Recognition is by comparison of two Iris Codes’ Hamming Distance (HD) to test the statistical
independence (FBI, 2018).
Advantages
The technology is accurate with a very limited false acceptance and rejection, highly
scalable for both large and small scale programs. It is also easy to use compared to the other
biometric systems (FBI, 2018).
Disadvantages
Iris recognition is expensive because of the cost of iris scanners. Moreover, some people
are not steady in front of the camera. That makes it difficult to complete the scanning in one shot
(FBI, 2018).

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Application
Used in finance and banking to identify people other than using cumbersome and time
consuming PINs and passwords. Also used in healthcare and welfare, immigration and border
control (FBI, 2018).
Privacy-enhancing technologies (PETs)
Communication Anonymizers
Anonymizers are the oldest private autonomy enhancing technologies. They deal with
privacy and shopping problems. The technology is concerned with micro data scrutiny which
browsers are subjected to when the access servers (Shukla & Sadashivappa, 2014). Again, it does
not provide shopping support because that requires transactional data. There are things that the
anonymizers are not concerned with like preventing prospect contact from merchants or
spammers, customers’ data and dispute resolution. The anonymizer is a better data manager in
ways like anonymizing email accounts. The anonymizer works in two ways to offer privacy;
providing a proxy server and providing security solutions at the PC level and offer extra security
to its web-based services like cookie management software and auditing tools. Most of
anonymizer’s sources are offered for download and examination. Lastly, it encrypts
transmissions from users and therefore prevents the owner’s intranet from observing web-based
interactions (Shukla & Sadashivappa, 2014).
Zero Knowledge
Zero knowledge is another private autonomy enhancing technology and exclusive in that
it only works with an open source principle. This technology understands that privacy is a right
and therefor it gives an individual 100% control over their own information plus protection
against other network service providers. Zero knowledge is also unique by allowing total

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anonymity of the customer to itself and to the merchant. Like the anonymizer, the software is can
be downloaded and is subject to full user scrutiny. It is easily usable and has little switching cost
and it is also very interoperable that is consistent with ZNK’s principles. The technology has the
best cryptographic technological potential for atomic and anonymous transactions (Jawurek,
Kerschbaum, & Orlandi, 2013).
Iprivacy
This seclusion enhancing technology provides a substitution for shopping and browsing
(Damiani, Pagano, & Pagano, 2015). Using the propriety technology, Iprivacy can define cases
of clear conflicting statements without another comment. To add on that, it is freely accessible
information that is used in the analysis. This privacy technology is a possibility of private
internet even when the world is offline. Iprivacy is not fully able to view all customer data and
does not have any business strategy to gain profit from any compilation of data (Yu, Zhang,
Kuang, Lin, & Fan, 2017). It is also possible to download softwares for shipping and transaction
companies. The shipping companies licenses it for customer privacy enhancement.
Wireless Sensors Network Threats and Vulnerabilities
WSN are highly vulnerable to many security attacks cause to the broadcast. The threats
and attacks include:
Sybil Attack
In this type of attack, a node presents itself in different identities to other nodes in the
network. Here, a computer is hijacked to claim several identities (Alajmi, 2014). The attack is
based on the fact that a computer network cannot ensure that each of the computing basics is
dissimilar. Sybil attack attacks by degrading the integrity of data, security as well as resource
utilization which distributed algorithms attempts to achieve. Common Sybil attack is on an

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internet poll which is engineered using numerous IP addresses to yield a huge number of votes
and using Sybil attacks to earn high rankings on Google Page Rank (Alajmi, 2014).
Recommendations on Mitigation of the Sybil Attack
One is by verification and encryption techniques to prevent launching of the attack by an
outsider on the WSN. Insider attacks are protected by use of public key cryptography to verify
identities. Resource testing is also an option used to define the resources from a collection of
identities (Alajmi, 2014).
Wormhole Attack
In this type of threat, the invader records the packet at one location in the network and
channels them to a new location. Bits are transmitted selectively. This attack needs no
compromising sensor in the network or other. The attack can be implemented at the early phase
when the sensor takeoff to look for information (Alajmi, 2014).
Recommendations on Mitigation of the Wormhole Attacks
One way of mitigating wormhole attacks is by a countermeasure called mobiworp that
keeps the drawbacks and mitigates the wormhole attack in mobile networks. Other techniques
include reducing request packet delays, using statistical profiling and stimulating analysis of
packet filters (Lee, Clark, Bushnell, & Poovendran, 2014).
Denial of Service Attack
Denial of service attacks disturb wireless transmission and occur either accidentally in the
form of interference, collusion or noise at the context of attacks or at the receiver side Zhang,
Cheng, Shi, & Chen, 2015). The target to reach is network access, infrastructure and server
application. The attack consumes the available resources for the target by transporting extra

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unnecessary data. Users cannot access services when there is a DoS attack. It is created in
different layers and its execution is by malicious flooding and desynchonization (Alajmi, 2014).
Recommendations on Mitigation of the DoS
The attack can be countered by paying for network resources, strong authentication,
identifying traffic and pushback. Securing the reprogramming process is also a protective
measure (Alajmi, 2014).
Conclusion
The different ways of mitigating WSN threats, PETs as well as the biometric technology have
been discussed in their respective sections as per the question demands. Following the discussion
above can lead to better risk management.

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References
Abdolahi, M., Mohamadi, M., & Jafari, M. (2013). Multimodal Biometric system Fusion Using
Fingerprint and Iris with Fuzzy Logic. International Journal Of Soft Computing And
Engineering (IJSCE), 2(6), 504-510. Retrieved from
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.301.2635&rep=rep1&type=pdf
Alajmi, N. (2014). Wireless Sensor Networks Attacks and Solutions. (IJCSIS) International
Journal Of Computer Science And Information Security,, 12(7), 1-4. Retrieved from
https://arxiv.org/ftp/arxiv/papers/1407/1407.6290.pdf
Bolle, R., Connell, J., Pankanti, S., Ratha, N., & Senior, A. (2013). Guide to biometrics (pp. 1-
46). New York, NY: Springer Science & Business Media.
Daluz, H. (2014). Fundamentals of fingerprint analysis (pp. 66-265). CRC Press.
Damiani, E., Pagano, F., & Pagano, D. (2015). iPrivacy: a distributed approach to privacy on the
cloud. arXiv preprint arXiv:1503.07994.
DIANE Publishing. (2018). Technology assessment using biometrics for border security. (p.
131).
FBI. (2018). Iris Recognition. Retrieved from https://www.fbi.gov/file-repository/about-us-cjis-
fingerprints_biometrics-biometric-center-of-excellences-iris-recognition.pdf/view
Jain, A., Nandakumar, K., & Ross, A. (2016). 50 years of biometric research: Accomplishments,
challenges, and opportunities. Pattern Recognition Letters, 79, 80-105. doi:
10.1016/j.patrec.2015.12.013
Jawurek, M., Kerschbaum, F., & Orlandi, C. (2013, November). Zero-knowledge using garbled
circuits: how to prove non-algebraic statements efficiently. In Proceedings of the 2013
ACM SIGSAC conference on Computer & communications security (pp. 955-966). ACM.

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Lee, P., Clark, A., Bushnell, L., & Poovendran, R. (2014). A passivity framework for modeling
and mitigating wormhole attacks on networked control systems. IEEE Transactions on
Automatic Control, 59(12), 3224-3237.
NSCT. (2018). Hand Geometry. Retrieved from https://www.fbi.gov/file-repository/about-us-
cjis-fingerprints_biometrics-biometric-center-of-excellences-hand-geometry.pdf/view
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Shukla, S., & Sadashivappa, G. (2014, March). A distributed randomization framework for
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Yu, J., Zhang, B., Kuang, Z., Lin, D., & Fan, J. (2017). iPrivacy: image privacy protection by
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Zhang, H., Cheng, P., Shi, L., & Chen, J. (2015). Optimal denial-of-service attack scheduling
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