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 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).
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).
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 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).
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.
Hand geometry recognition has a long history from the 1980s (NSTC, 2018)
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).
According to NSTC, the technology is applicable for physical access, attendance tracking, and personal verification especially in security and accountability sectors (NSTC, 2018).
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.
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).
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).
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 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 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).
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.
WSN are highly vulnerable to many security attacks cause to the broadcast. The threats and attacks include:
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 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).
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).
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).
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 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 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).
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.
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 https://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.
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
Saito, T., & Soliven, M. (2014). U.S. Patent No. 8,899,487. Washington, DC: U.S. Patent and Trademark Office.
Shukla, S., & Sadashivappa, G. (2014, March). A distributed randomization framework for privacy preservation in big data. In IT in Business, Industry and Government (CSIBIG), 2014 Conference on (pp. 1-5). IEEE.
Yu, J., Zhang, B., Kuang, Z., Lin, D., & Fan, J. (2017). iPrivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Transactions on Information Forensics and Security, 12(5), 1005-1016.
Zhang, H., Cheng, P., Shi, L., & Chen, J. (2015). Optimal denial-of-service attack scheduling with energy constraint. IEEE Transactions on Automatic Control, 60(11), 3023-3028.
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