IT Risk Management Report: Biometrics, WSN, and PETs Analysis

This report delves into the multifaceted domain of IT risk management, encompassing a comprehensive analysis of biometric systems, privacy-enhancing technologies (PETs), and wireless sensor networks (WSNs). The report begins by exploring various biometric systems like palm print, face recognition, and gait recognition, detailing their underlying technologies, advantages, disadvantages, and practical applications. Subsequently, it transitions to discuss PETs, such as encryption, metadata, and application programming, highlighting their role in enhancing internet privacy. Finally, the report examines wireless sensor networks (WSNs), dissecting their architecture, protocol stack, and potential vulnerabilities, including denial-of-service and wormhole attacks, while proposing mitigation strategies. The report concludes with a discussion on the threats and vulnerabilities associated with WSNs and provides recommendations for mitigating these risks, offering a holistic view of IT security and privacy concerns.

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

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Table of Contents
Question 1..................................................................................................................................2
Question 2..................................................................................................................................7
Question 3..................................................................................................................................8
References................................................................................................................................10

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Question 1
Research into the different types of biometric systems, which are currently available for
access control systems. Briefly describe the techniques and technologies used for each type
and discuss the advantages and disadvantages for the three types below. Give an example
application when each type could be used: Palm print, Face recognition and Gait
recognition.
Answer: A specific biometric system is the technological system, which usually utilizes
information regarding any particular person for his/her unique identification. This biometric
system always relies on data or information regarding all the unique biological characteristics
for working effectively (Rigas, Economou & Fotopoulos, 2012). The biometric systems
always include the running data with the help of algorithms for any typical outcome that is
solely related to the unique identification of the user. Biometrics is the technical terms for all
types of calculations and measurements of body. This system of biometrics is completely
related to the characteristics of human beings. The biometrics authentication or the realistic
authentication is utilized for the identification of any human being (Cappelli, Ferrara & Maio,
2012). It even helps in the identification of individuals in proper groups, who are taken under
surveillance. The identifiers of the biometric systems are absolutely distinctive and
measurable features that are utilized for labelling and describing the individuals. They are
often sub divided as physiological and behavioural features. The various physiological
features of a human being mainly include fingerprint recognition, hand geometry, face
recognition, voice recognition, retina recognition, palm veins, gait recognition, DNA
recognition and many more (Banerjee & Woodard, 2012). The details of the above mentioned
biometric systems are as follows:

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i) Fingerprint Recognition: This is the most basic system in biometrics. Each and
every person has their own biological trait and fingerprint. Therefore, two persons cannot
have the same fingerprint. Thus, recognition of the fingerprint can be undertaken as the most
secured system of biometrics and unique identification. A fingerprint within the narrow logic
is nothing but an impression that is eventually left by all the friction edges of any specific
finger of a human being (Banerjee & Woodard, 2012). The steps that are utilized in the
identification of the fingerprint mainly include image acquisition, image enhancement,
feature extraction and pattern recognition. The image acquisition is done with the help of
various sensors. All the captured images are eventually blurred or contain noises. They affect
the quality of the fingerprint image. The image of the fingerprint taken always varies by the
location where the finger is placed, the direction and even the stretching degree. The second
step is the image enhancement (Bringer, Chabanne & Patey, 2013). Most of the time, the
image is corrupted through several types of noises like holes, smudges and creases. The
image of the fingerprint is enhanced in this particular step. The third step is the feature
extraction. Every fingerprint is pattern has various kinds of features. These typical features of
the fingerprint are extracted in this step. The final step is the pattern recognition. A pattern is
the proper collection of the descriptors (Rigas, Economou & Fotopoulos, 2012). This pattern
is featured by the elements order, rather than through the intrinsic nature of all the elements.
The recognition of pattern is sub divided into two distinct parts, like the Decision theoretic
and Structural. The Decision theoretic part mostly deals with all the patterns that are
described by utilizing quantitative descriptors like area, texture and length. The category of
structural mainly deals with the patterns that are described by the utilization of qualitative
descriptors (Cappelli, Ferrara & Maio, 2012). The two methods or techniques that are utilized
in fingerprint recognition are as follows:

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a) Pattern Based Method: The pattern based method is applied by accessing various
key patterns, which eventually involve arches, whirl pools and loops.
b) Minutia Based Method: This is the second method that is applied to the fingerprint
system. This particular algorithm is broadly utilized for the fingerprint authentication (Rigas,
Economou & Fotopoulos, 2012). This algorithm mainly focuses on the ending of bifurcations
and the ridges.
ii) Face Recognition: This is the second most important system of biometrics. The
face of a human being is easily detected or identified with the help of the face recognition
system. This face recognition system is utilized by many organizations for their employee
entrance (Beveridge et al., 2013). This face recognition system is extremely effective if and
only if the person has an identical twin. Moreover, this particular biometric system is
responsible for all types of intrusion prevention. The most popular algorithms for recognition
are the principal component analysis by utilizing eigen faces, linear discriminate analysis,
matching of elastic bunch graph by utilizing Fisher face algorithm, the Markov model that is
hidden, multi linear subspace learning by utilizing the representation of tensor and even the
matching of the neuronal motivated dynamic link (Bringer, Chabanne & Patey, 2013). These
face recognition methods are the best ways for any type of unique identification of a human
being.
iii) Palm Print: This is the third type of biometric system. A palm print typically
means a specific image that is acquired from the region of palm of the hand. This palm print
is either taken by a scanner, which is an online image or simply an offline image. The offline
image is taken with paper and ink (Cappelli, Ferrara & Maio, 2012). The palm of a human
being comprises of several lines or wrinkles. It even has epidermal ridges. The main
difference between fingerprint and palm print is that palm print comprises of marks, indents

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and even texture. It is more informative in comparison to fingerprint. However, there is a
major advantage that no two palms are similar. Each and every palm differs in the lines or
wrinkles. The most utilized techniques of palm print recognition are as follows:
a) Palm Line Matching: This is the most utilized technique in palm print recognition
(Rigas, Economou & Fotopoulos, 2012). The lines present in the palm are matched in this
method and if any type of dissimilarity is found out, the system does not allow the person to
enter into the building.
b) Skin Colour Thresholding: For perfect segmentation of the human palm from the
background, this particular technique is utilized. As soon as the segmentation of the human
palm is done, it is automatically turned into the binary form.
iv) Gait Recognition: This is the fourth most popular form of unique identification of
human beings. Gait recognition or gait analysis is the proper systematic study of locomotion
of animals, specifically the study of the motion of human beings. This analysis is done by
utilizing the brain or eyes of the observers and by instrumentation for the measurement of the
movements of the body and the muscle activities (Fragkiadakis, Tragos & Askoxylakis,
2013). There are various types of methods of gait recognition biometric systems. All these
technique are extremely popular for the users, who are utilizing biometric systems. Tracking
of silhouettes is an important technique of gait recognition.
i) Fingerprint Recognition: This particular biometric system has several advantages
and disadvantages. The major advantages of fingerprint recognition are as follows:
a) Accurate: Fingerprint recognition is extremely accurate and there is highly any
problem.
b) Cost Effective: It is extremely cost effective and can be utilized by all.

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c) Easy Implementation: This is the third benefit. It is extremely easy to implement
and can be easily used.
d) Smaller Storage Space: It does not require huge space and thus utilize only smaller
storage space.
The main disadvantages of the fingerprint recognition are as follows:
a) Problems for Handicapped Persons: Physically disabled people cannot use this
technique and thus it is a major disadvantage.
b) False Rejections: This system often testifies false identifications.
ii) Palm Print: Palm print biometrics also has few advantages and disadvantages.
They are as follows:
The main advantage of palm print recognition is that as the area of the palm is
extremely large, more information can be gathered from palm print, in comparison to a
fingerprint (Cappelli, Ferrara & Maio, 2012).
The main disadvantage of palm print recognition is that the scanners of palm print are
extremely bulky in size.
iii) Gait Recognition: The gait recognition technique has various advantages and
disadvantages. They are as follows:
The main advantage of gait recognition is that it is extremely cost effective and does
not incur huge cost in comparison to others.
The main disadvantage of this particular technique is that in some of the methods like
tracking silhouettes, gives vague results.

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Question 2
Other than security concerns, privacy are considered a core value and are recognized
either implicitly or explicitly as a fundamental human right. Privacy-enhancing
technologies (PETs) can be defined as technologies that enforce legal privacy principles in
order to protect and enhance the privacy of users of information technology. Research and
discuss three PETs which can be used on the Internet. Use examples to illustrate your
answer.
Answer: Privacy enhancing technologies or PETs increments the entire protectiveness of
information security by simply ensuring that no confidential data is lost (De Cristofaro &
Wright, 2013). The various types of privacy enhancing technologies help in the protection of
all sorts of confidential information or data. The three PETs that can be utilized on the
Internet are as follows:
i) Encryption: Encryption is the most utilized and popular technique for securing the
confidentiality of data or information. This technique supports the security and even the
proportionality theory of the law of data protection (John Justin & Manimurugan, 2012). It is
extremely simple in implementation and hence is termed as one of the most effective tool of
privacy enhancing technologies. Data Encryption Standard is an example of privacy
enhancing technologies.
ii) Metadata and Digital Rights Management: This is the most recent technology,
when compared to encryption. This privacy enhancing technologies tool gives a proper
framework, which describes the data semantics. These data semantics are secured over the
Internet (Foulonneau & Riley, 2014). Therefore, it is extremely useful for obtaining all types
of compliance policies with the legislation of data protection, since it helps in the
differentiation between personal data and public data. Utilization of several cryptographic

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algorithms in case of protection of data is an important example of privacy enhancing
technologies.
iii) Application Programming: According to this particular tool of privacy enhancing
technologies, the various packages of software, which process data eventually have to follow
basic rules and regulations of processing of data and fulfils the requirement of privacy
protection (Aloul et al., 2012). As a result, the users are not breached, when they are using an
application program on the Internet.
Question 3
Wireless sensor networks (WSNs) can be described as a network of nodes that makes a
collaborative effort in sensing data around its periphery and its surrounding environment.
Research into the area of WSNs and understand the WSN architecture and protocol stack.
Discuss three different types of threats and vulnerabilities, which can be used to attack the
WSN. Give your recommendations on how the threats and vulnerabilities can be mitigated
for the WSN.
Answer: The three different types of threats or vulnerabilities, which can be used for
attacking the Wireless Sensor Networks, are as follows:
i) Denial of Service Attacks: This is one of the most vulnerable attacks, where the
attacker or the intruder sends unnecessary extra data packets to the users (Yu, 2014). This in
case prevents the original users to access the particular resources, which they are intended to
send.
ii) Wormhole Attacks: This is the second most vulnerable attack for wireless sensor
networks. This type of attack occurs in the first phase of setting of connections (Khan et al.,

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2013). The intruder stores all the data packets in a specific location and further tunnelling
them to all other locations. The other locations often contain malicious nodes.
iii) Sybil Attacks: This is the third type of attack in wireless sensor network and it
mainly targets several routing features and degrades the data integrity.
Threats and vulnerabilities can be mitigated in wireless sensor networks. Two
possible recommendations for mitigating the threats and vulnerabilities are establishing
intrusion detection system and entrance of foreign elements in any network.

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References
Aloul, F., Al-Ali, A. R., Al-Dalky, R., Al-Mardini, M., & El-Hajj, W. (2012). Smart grid
security: Threats, vulnerabilities and solutions. International Journal of Smart Grid
and Clean Energy, 1(1), 1-6.
Banerjee, S. P., & Woodard, D. L. (2012). Biometric authentication and identification using
keystroke dynamics: A survey. Journal of Pattern Recognition Research, 7(1), 116-
139.
Beveridge, J. R., Phillips, P. J., Bolme, D. S., Draper, B. A., Givens, G. H., Lui, Y. M., ... &
Flynn, P. J. (2013, September). The challenge of face recognition from digital point-
and-shoot cameras. In Biometrics: Theory, Applications and Systems (BTAS), 2013
IEEE Sixth International Conference on (pp. 1-8). IEEE.
Bringer, J., Chabanne, H., & Patey, A. (2013). Privacy-preserving biometric identification
using secure multiparty computation: An overview and recent trends. IEEE Signal
Processing Magazine, 30(2), 42-52.
Cappelli, R., Ferrara, M., & Maio, D. (2012). A fast and accurate palmprint recognition
system based on minutiae. IEEE Transactions on Systems, Man, and Cybernetics,
Part B (Cybernetics), 42(3), 956-962.
De Cristofaro, E., & Wright, M. (2013). Privacy enhancing technologies. In Proceedings of
13th International Symposium, PETS.
Foulonneau, M., & Riley, J. (2014). Metadata for digital resources: implementation, systems
design and interoperability. Elsevier.

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Fragkiadakis, A. G., Tragos, E. Z., & Askoxylakis, I. G. (2013). A survey on security threats
and detection techniques in cognitive radio networks. IEEE Communications Surveys
& Tutorials, 15(1), 428-445.
John Justin, M., & Manimurugan, S. (2012). A survey on various encryption
techniques. International Journal of Soft Computing and Engineering (IJSCE)
ISSN, 2231, 2307.
Khan, W. Z., Aalsalem, M. Y., Saad, M. N. B. M., & Xiang, Y. (2013). Detection and
mitigation of node replication attacks in wireless sensor networks: a
survey. International Journal of Distributed Sensor Networks, 9(5), 149023.
Rigas, I., Economou, G., & Fotopoulos, S. (2012). Biometric identification based on the eye
movements and graph matching techniques. Pattern Recognition Letters, 33(6), 786-
792.
Yu, S. (2014). Distributed Denial of Service Attack and Defense (pp. 15-29). Springer New
York.