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N32012 Risk Management In IT Technologies

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Course Code: N32012
University: University Of The West Of England

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Country: United Kingdom


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.
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.


Biometric Systems
A biometric system could be defined as a technological system that would make use of information regarding to a certain individual in order to identify them. These highly designed systems mainly depend on some specific points that would include the biological characters for the efficiency within their work. The biometric system would involve the processing of data with algorithms in order to derive a particular result (Gomez-Barrero, Galbally & Fierrez, 2014).  
The different types of biometric systems, which are broadly used are:
Fingerprint – It is a process of comparison a fingerprint against another fingerprint for the purpose of determining the impressions would be from the same palm or finger. The recognition could be defined with the use of Robust Local Features. These techniques make use of minutiae points in order to match and represent the fingerprint image.  
The advantages of fingerprint recognition is that they offer very high accuracy systems and is one of the most economical authentication technique. The disadvantages of fingerprints are that hackers might be able to deceive and thus outsmart the machines, while targeting the fingerprints on another surface (Marasco & Ross, 2015).  
Hand Geometry – This form of recognition system are mainly based on various forms of measurements that would be taken from the hands of humans that would majorly include the shape, size, length and width of palm. Since there is no impact of the factors of environment, hence there are no negative impacts on the accuracy of authentication.
The advantages of these systems are that they are simple, ease of use and inexpensive. The data that would be stored would be very easy to collect. These systems are also considered to be less intrusive than the fingerprints or retinal systems (Das & Meshram, 2013). The disadvantages of hand geometry biometrics is that these geometry are not unique and hence cannot be used within systems of identification. The size of the data based on these geometrics is huge and thus is not suitable for embedded systems.
Iris Recognition – This could be defined as an automated systems of biometric identification. This method makes use of mathematical pattern recognition systems and techniques based on video images. This system also employs the use of camera technology that would have delicate near infrared illumination in order to acquire the details of intricate structures.    
The advantages of Iris recognition is that it provides high level of accuracy, highly scalable as it can be used in small and large scale programs. The iris scanning can be performed from a certain distance unlike retina scanning. The patterns of the iris remain stable throughout the lifetime of a person. These form of recognition system is easy to plug and play when compared to the modalities of biometric recognition (Bowyer & Burge, 2016). The disadvantages of iris recognition system is that they are highly expensive compared to the other forms of biometric modalities. In some cases, there is some problem within the scanning of iris as the person has to be steady in front of the device. The presence of any form of reflections during the scanning would affect the entire procedure.
Privacy Enhancing Technologies (PET)
The PET could be defined as a consistent term, which generally refers to some specific methods that would act accordingly within the defined law of protection of data. The PET would permit the user for the protection of their privacy based on the personal identifiable information (PII) that are mainly handled by various applications or services (Hoepman, 2014).
The three PETs, which can be used on the Internet are:

Communication Anonymizers– These are those technologies that are able to hide the online identity of a user and thus replace them with a duplicate identity. An anonymizer could be defined as a proxy tool that would attempt to make the activities on the web to be untraceable. This tool could be defined as a computer within the proxy server that would be considered as an intermediary and would perform as a shield of privacy in between the computer of client and the rest of internet. This tool could also be used to get access to the internet on the behalf of the user and thus protect their anonymity. These anonymizers could be applied to email contents, VoIP, Chat systems, instant messaging and browsing of the internet (Palamidessi, 2015).
Enhanced Privacy ID (EPID)– This is defined as an algorithm based on digital signatures, which have the capability to support the property of anonymity. Unlike the traditional algorithms based on digital signature where the unique identity would support a unique key of public verification and a unique private signature key, the EPID helps the users in providing a common key based on public verification. This would be thus associated with different unique private signature key. This form of system is an upgraded version of Direct Anonymous Attestation (DAA) algorithm. This algorithm is high successful in supporting the property of anonymity. The system of EPID enhances the facilities of DAA as it provides a complementary utility function as they are able to revoke a certain private key (Chaudhry et al., 2015).     

The EPID mainly comprises of internationally set standards and Trusted Computing Group in order to provide services based on authentication of intellectual property.

Obfuscation– This would generally refer to the several form of practices based on the addition of distracting and misleading data to a certain profile or log account. Though the effectiveness of the system of obfuscation is still doubted, it has a greater impact in the field of shallow algorithms (Sahai & Waters, 2014).

Wireless Sensor Networks (WSN)
These could be defined as such type of networks that would be able to communicate the gathered information from several monitored fields with the help of wireless links. The data would then be forwarded through several nodes. The data would be communicated to other form of networks with the help of a gateway such as the wireless Ethernet. The WSN is a wireless form of network that is mainly used for monitoring the environmental and physical conditions such as temperature, pressure and sound and thus passes the data with the help of a network to a certain location (Yang, 2014).
The WSN Architecture
The WSN follows the OSI model of architecture. This includes three cross layers and five layers. The three cross planes mainly include mobility management, power management and task management. The layers of WSN are mainly used to enable the sensors to function together for raising the full efficiency within the network (Tomovic & Radusinovic, 2015).
The WSN protocol stack comprises of data link layer, network layer, transport layer, physical layer and application layer.
Data Link Layer – This layer holds the duty for multiplexing the streams of data, detection of data frame, access of medium and control the errors. This layer also ensures the establishment of communication resources for the transfer of data.
Network Layer – This layer would be responsible for data routing that would be supplied by transport layer. The efficiency of power is an important aspect to be considered. Other features of the network layer includes fault tolerance, clock synchronization and many others (Sunshine, 2013).
Application Layer – This layer is responsible for the building of different types of application based software and thus to be used within the application layer. The protocols within the application layer would include the transparency within the software and hardware (Karagiannis et al., 2015).
Transport Layer – This layer would be helpful in maintaining the data flow if required by the application of the sensor network. This layer would also be required to be accessed with the help of internet or other forms of external networks.
Physical Layer – This layer is responsible for the selection of frequency, generation of carrier frequency, detection of signals, encryption of data and modulation of data. The modulation would mainly depend on design of hardware constraints and transceiver that would aim for consumption of low power, simplicity and low cost per unit (Mukherjee et al., 2014).
The different types of threats and vulnerabilities that affect the WSN systems are:
Jamming – This is a form of attack in which the attacker would be able to broadcast a high form of energy signal. The attack would be constant and would corrupt the packets. The attack would be able to randomly alternate between jamming and sleep in order to save energy.
Radio Interference – In this attack, the adversary would produce large amounts of interference in an intermittent manner.
Continuous Access of Channel – The malicious node would be able to disrupt the Media Access Control (MAC) protocol. This could be achieved by continuously sending a request or transmitting over the channel. This would ultimately lead to a starvation for attacking other available nodes within the network (Ren et al., 2016)  
Some of the recommendations, which can be suggested for preventing the threats and vulnerabilities within the WSN are: The sensors should be properly tuned. Symmetric key algorithms should be properly used with which the disclosure of information of the keys would be delayed by a certain interval of time. Limitation within the rate of request should be made such that the network would be able to ignore excessive amount of data requests. Error-correcting codes should also be installed within the networks (Han et al., 2014).  
Bowyer, K. W., & Burge, M. J. (Eds.). (2016). Handbook of iris recognition. Springer London.
Chaudhry, S. A., Farash, M. S., Naqvi, H., Kumari, S., & Khan, M. K. (2015). An enhanced privacy preserving remote user authentication scheme with provable security. Security and Communication Networks, 8(18), 3782-3795.
Das, P., & Meshram, S. (2013). An efficient hand-geometry system for biometric identifications. IOSR J Electron Commun Eng, 4(4), 17-9.
Gomez-Barrero, M., Galbally, J., & Fierrez, J. (2014). Efficient software attack to multimodal biometric systems and its application to face and iris fusion. Pattern Recognition Letters, 36, 243-253.
Han, G., Jiang, J., Shu, L., Niu, J., & Chao, H. C. (2014). Management and applications of trust in Wireless Sensor Networks: A survey. Journal of Computer and System Sciences, 80(3), 602-617.
Hoepman, J. H. (2014, June). Privacy design strategies. In IFIP International Information Security Conference (pp. 446-459). Springer, Berlin, Heidelberg.
Karagiannis, V., Chatzimisios, P., Vazquez-Gallego, F., & Alonso-Zarate, J. (2015). A survey on application layer protocols for the internet of things. Transaction on IoT and Cloud Computing, 3(1), 11-17.
Marasco, E., & Ross, A. (2015). A survey on antispoofing schemes for fingerprint recognition systems. ACM Computing Surveys (CSUR), 47(2), 28.
Mukherjee, A., Fakoorian, S. A. A., Huang, J., & Swindlehurst, A. L. (2014). Principles of physical layer security in multiuser wireless networks: A survey. IEEE Communications Surveys & Tutorials, 16(3), 1550-1573.
Palamidessi, C. (2015, April). Quantitative approaches to the protection of private information: State of the art and some open challenges. In International Conference on Principles of Security and Trust (pp. 3-7). Springer, Berlin, Heidelberg.
Ren, J., Zhang, Y., Zhang, N., Zhang, D., & Shen, X. (2016). Dynamic channel access to improve energy efficiency in cognitive radio sensor networks. IEEE Transactions on Wireless Communications, 15(5), 3143-3156.
Sahai, A., & Waters, B. (2014, May). How to use indistinguishability obfuscation: deniable encryption, and more. In Proceedings of the forty-sixth annual ACM symposium on Theory of computing (pp. 475-484). ACM.
Sunshine, C. A. (Ed.). (2013). Computer network architectures and protocols. Springer Science & Business Media.
Tomovic, S., & Radusinovic, I. (2015, November). Performance analysis of a new SDN-based WSN architecture. In Telecommunications Forum Telfor (TELFOR), 2015 23rd(pp. 99-102). IEEE.
Yang, K. (2014). Wireless sensor networks. Principles, Design and Applications.

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