Inf80043 It Risk Management - Assessment Answers

Your task in this section is to prepare a response to each of the questions. Please refer to the marking guide when preparing your response to see what criteria will be used to assess your work and your progress towards the learning outcomes.

 

a) Research into the different types of bio-metric 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: 
Fingerprint 
Hand Geometry
Iris Recognition  
b) Other than security concerns, privacy is considered a core value and is 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. 
c) 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.

Question 1

Various Types of the Biometric System with Advantages, Disadvantages and Examples 

There are several types of biometric authentication system, which are:

1. i) Face Recognition Systems: This is the first and the most important type of biometric authentication system (Bhagavatula et al., 2015). It is responsible for helping in the significant verification or even better identification of that specific individual from the various digitalized images; either by simply analysis of those images or by comparing the patterns in the face image.

There are some of the major techniques, which are eventually used for this face recognition system, which are:

1. a) Geometry Based: The very first technique of the face recognition system is based on the face geometry. The fewer relative poses and the edge detection can be easily and promptly executed by taking the help of this technique (Awasthi & Srivastava, 2013). Three distinct parts of a face are being considered here for face matching like eyes, mouth and nose.
2. b) Feature Based: This particular technique is responsible for matching eyes, lips and nose of the particular individual. These three parts are used as the typical predominant aspects for the major purpose of finding the appropriate face.
3. ii) Fingerprint Recognition Systems: The second important type of the biometric authentication system is the fingerprint recognition system (Sayed et al., 2013). This specified biometric system subsequently takes down the respective fingerprint images of the individual and next the basic features such as loops, whorls and arches are recorded only after undertaking the various outlines like furrow, edge and minutiae.

There are some of the major techniques, which are eventually used for this fingerprint recognition system, which are:

1. a) Correlation: The very first technique of the fingerprint recognition system is the correlation that could overlay the two typical images of fingerprints by simply associating within the respective equivalent pixels (Klonovs et al., 2013).
2. b) Minutiae: The second significant technique of this fingerprint recognition system is minutiae, which has the ability of storing the plane, when the point set is being included within the input output minutiae.

iii) Voice Recognition Systems: This is the third and another important type of biometric authentication system (Bhatt & Santhanam, 2013). The voice recognition systems could be solely utilized for the proper production of speech patterns by simply combining both the physiological as well as behavioural features. These specific physiological and behavioural features could be eventually by proper speech technology processing.

There are some of the major techniques, which are eventually used for this voice recognition system, which are:

1. a) Automated Detection of the Voice Signal: The first technique of a voice recognition system helps in easy and automatic detection of various signals of voices of the individuals (Abo-Zahhad, Ahmed & Abbas, 2014).
2. b) Feature Extractions: This is the next technique for the biometric type of voice recognition systems. Here, the features of the voice of any specific person are being extracted for proper identification and verification.
3. iv) Iris Recognition System: The fourth and one of the most important kind of this biometric authentication system is these iris recognition systems (Chen, Pande & Mohapatra, 2014). It helps in the perfect identification as well as verification of the authenticated persons, which is eventually done on the basis of the single pattern within their ring shaped region in a person’s eyes’ pupil. This particular technique even helps in checking and verifying the colours of their eyes.

There are some of the major techniques, which are eventually used for this iris recognition system, which are:

1. a) Normalization:  This is the first and the foremost technique for the specified iris recognition systems (Chaudhry et al., 2015). In normalization technique, a remapping is being done for their iris regions by taking the core help of non centric, normalized and polarized representations.
2. b) Segmentation: This is the second important or vital technique for the iris recognition systems. Similar to its name, this particular technique helps in perfect segmentation of the specified iris of the authenticated person to small circular images (Nandi et al., 2014). Hence, it is much effective for the perfect verification of each and every individual.
3. v) Hand Geometry Systems: Another popular and noteworthy type of biometric authentication system is the hand geometry. In this particular type of biometric system, a proper verification of the person is done by following a simplified identification process of various hand shapes (Peng et al., 2014). The hand geometry hence can be properly measured by taking the core help of each and every dimension and then all of these dimensions are being compared to the previous measurements.

There are some of the major techniques, which are eventually used for this hand geometry system, which are:

1. a) False Acceptance Rates: This particular technique is responsible for evaluating the total ratios of all the unauthorized numbers of user with the summation of the attempts made.
2. b) False Rejection Rates: This is the second technique of hand geometry system, which is responsible for evaluating the total ratios of all the authorized numbers of user with the summation of the attempts made (Roth et al., 2013). These authorized users are eventually rejected by the biometric system.
3. c) Equal Error Rates: This is the last or the final technique for hand geometry system that subsequently matches the rate of false acceptance with the rate of false rejection.

The benefits and the disadvantages of three distinct types of biometric authentication system are given below:

1. i) Fingerprint Recognition Systems: There are various important and noteworthy advantages of the biometric authentication system of fingerprint recognition system, which are:
2. a) Better Cost Effectiveness: This the major and the most important advantage of the fingerprint recognition system (Murillo-Escobar et al., 2015). It provides better cost effectiveness and hence any user can easily afford this system without much complexity.
3. b) Higher Security: Another important advantage of the fingerprint recognition system is that it provides higher security to information and data.
4. c) Simplified Architecture: The next important and noteworthy benefit of this fingerprint recognition system would be the entire architecture or the structural design is quite simplified (Sizov, Lee & Kinnunen, 2014).

In spite of having these advantages, few disadvantages are also present for this particular system, which are:

1. a) Inaccuracy in Data: Fingerprint recognition system eventually provides inaccuracy in the data for its users.   
2. b) Needs Extra Hardware: Fingerprint recognition system needs the proper deployment of an extra hardware and without this deployment; it is not possible to use this system (Chun et al., 2014).

The basic application of the fingerprint recognition systems is when the registration as well as identification of a voter’s card is being done.

1. ii) Hand Geometry System: There are several major advantages of this particular biometric system, which are:
2. a) Cost Effectiveness: This is the first benefit of the hand geometry system would be that this provides cost effectiveness and thus affording this system is much easier.
3. b) Easier Data Collection: Data collection is much easier in this particular system of hand geometry (Nandi et al., 2014).

In spite of having these advantages, few disadvantages are also present for this particular system, which are:

1. a) Bulky Datum: The size of the data is extremely large and bulky.
2. b) Not Used Majorly: Another important demerit of hand geometry system is that it could not be used majorly by the users.

The example of application for hand geometry is either in school, colleges or offices.

iii) Iris Recognition Systems: There are several major advantages of this particular biometric system, which are:

1. a) Higher Security: Iris recognition system gives higher security for the confidential data.
2. b) AccurateData: This particular system of iris recognition always provides accuracy in data (Chaudhry et al., 2015).

In spite of having these advantages, few disadvantages are also present for this particular system, which are:

1. a) Expensive: Iris recognition is quite costly and thus it is not possible for everyone to afford it.
2. b) Obscure: This system is obscured often by lenses and eyelashes and this is the second demerit of iris recognition.

The example of the application of an iris recognition system is either in army or in police ground for proper verification.

Question 2

Three Privacy Enhancing Technologies or PETs with Examples

 There are three important PETs or Privacy Enhancing Technologies, which are:

1. i) Iaccurate Online Data: This is the first and one of the most popular Privacy Enhancing Technology. Whenever a user creates an account for the MSN, often inaccurate data is provided to him for security purposes. The wrong data is for name, address, ban details and contract details (Awasthi & Srivastava, 2013). Next, the identification and the password of that particular user are substantially published over the Internet. Hence, this particular user can promptly utilize his account, without even having any issue and moreover, he is being ensured that all the private data are absolutely secured and they are not at all shared on Internet. Furthermore, this particular PET also makes that sensitive data or information preserved within the database and then the data integrity is maintained.

The best example of wrong data online would be that a user is eventually sharing the forged data online for ensuring that his original data is safe or secured.

1. ii) Communcation Anonymizer: This particular Privacy Enhancing Technology helps to hide the original online identities by simply replacing the original identity with the non traceable identity (Bhagavatula et al., 2015). The communication anonymizer culd be applied within emails, instant messaging, P2P networking or several others.

The best example of this PET is the disguising of few IP addresses within anonymising networks.

iii) EPID: The EPID or Enhanced Privacy ID is the basic algorithm of any particular digital signature, which helps in supporting anonymity. Furthermore, the private signature keys and public verification keys are even provided by this EPID. It is created for giving a specific device to the external party and then identifies this type of device for using the privacy ID (Sayed et al., 2013). The major advantage of EPID would be that the original identity could be revealed by this PET and the privacy of the user’s data is not affected.

 The best example of this particular PET would be using PKI in the algorithm of digital signatures.

Question 3

Wireless Sensor Networks or WSN and the Three Threats for WSN with Proper Recommendations

The WSNs are collectiveness of few sensors, which is helpful to monitor as well as record the physical conditions. OSI architecture is being followed in the architecture of WSN. The architecture and protocol stack consists of the five cross and five other layers (Bhatt & Santhanam, 2013). The other five layers are physical, data link, network, transport and application and the cross five layers are power, mobility, task, QoS management and security management. All of these layers of WSN architecture and protocol stack are quite effective for the users.

There are three threats for the WSN architecture, which are:

1. i) Denial of Services: In this attack, the hacker seeks into machine to make it unavailable. This occurs within physical layer.
2. ii) Tampering: Within this attack, the respective adversary compromises the sensor node to use those nodes for misleading the network activity (Peng et al., 2014).

iii) Injection of Erroneous Data: The datum is routed in an erroneous manner and thus the last data is replayed.

The recommendations for these three threats are:

1. i) Denial of Services: Message prioritization is important to jam the spread spectrum in WSN.
2. ii) Tampering: Tamper proofing would be effective here.

iii) Injection of Erroneous Data: Cryptography is the best solution for removing this particular issue.

References

Abo-Zahhad, M., Ahmed, S. M., & Abbas, S. N. (2014). Biometric authentication based on PCG and ECG signals: present status and future directions. Signal, Image and Video Processing, 8(4), 739-751.

Awasthi, A. K., & Srivastava, K. (2013). A biometric authentication scheme for telecare medicine information systems with nonce. Journal of medical systems, 37(5), 9964.

Bhagavatula, R., Ur, B., Iacovino, K., Kywe, S. M., Cranor, L. F., & Savvides, M. (2015). Biometric authentication on iphone and android: Usability, perceptions, and influences on adoption.

Bhatt, S., & Santhanam, T. (2013, February). Keystroke dynamics for biometric authentication—A survey. In Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on (pp. 17-23). IEEE.

Chaudhry, S. A., Mahmood, K., Naqvi, H., & Khan, M. K. (2015). An improved and secure biometric authentication scheme for telecare medicine information systems based on elliptic curve cryptography. Journal of Medical Systems, 39(11), 175.

Chen, S., Pande, A., & Mohapatra, P. (2014, June). Sensor-assisted facial recognition: an enhanced biometric authentication system for smartphones. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services (pp. 109-122). ACM.

Chun, H., Elmehdwi, Y., Li, F., Bhattacharya, P., & Jiang, W. (2014, June). Outsourceable two-party privacy-preserving biometric authentication. In Proceedings of the 9th ACM symposium on Information, computer and communications security (pp. 401-412). ACM.

Klonovs, J., Petersen, C. K., Olesen, H., & Hammershoj, A. (2013). ID proof on the go: Development of a mobile EEG-based biometric authentication system. IEEE Vehicular Technology Magazine, 8(1), 81-89.

Murillo-Escobar, M. A., Cruz-Hernández, C., Abundiz-Pérez, F., & López-Gutiérrez, R. M. (2015). A robust embedded biometric authentication system based on fingerprint and chaotic encryption. Expert Systems with Applications, 42(21), 8198-8211.

Nandi, S., Roy, S., Dansana, J., Karaa, W. B. A., Ray, R., Chowdhury, S. R., ... & Dey, N. (2014). Cellular automata based encrypted ECG-hash code generation: an application in inter human biometric authentication system. International Journal of Computer Network and Information Security, 6(11), 1.

Peng, J., El-Latif, A. A. A., Li, Q., & Niu, X. (2014). Multimodal biometric authentication based on score level fusion of finger biometrics. Optik-International Journal for Light and Electron Optics, 125(23), 6891-6897.

Roth, J., Liu, X., Ross, A., & Metaxas, D. (2013, June). Biometric authentication via keystroke sound. In Biometrics (ICB), 2013 International Conference on (pp. 1-8). IEEE.

Sayed, B., Traore, I., Woungang, I., & Obaidat, M. S. (2013). Biometric authentication using mouse gesture dynamics. IEEE Systems Journal, 7(2), 262-274.

Sizov, A., Lee, K. A., & Kinnunen, T. (2014, August). Unifying probabilistic linear discriminant analysis variants in biometric authentication. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) (pp. 464-475). Springer, Berlin, Heidelberg.