1.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:
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 narrowlogic 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
Pattern Based Method: The pattern based method is applied by accessing various key patterns, which eventually involve arches, whirl pools and loops.
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.
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.
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 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:
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.
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.
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.
Fingerprint Recognition: This particular biometric system has several advantages and disadvantages. The major advantages of fingerprint recognition are as follows:
Accurate: Fingerprint recognition is extremely accurate and there is highly any problem.
Cost Effective: It is extremely cost effective and can be utilized by all.
Easy Implementation: This is the third benefit. It is extremely easy to implement and can be easily used.
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:
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.
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.
2.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:
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.
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 algorithms in case of protection of data is an important example of privacy enhancing technologies.
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.
3.The three different types of threats or vulnerabilities, which can be used for attacking the Wireless Sensor Networks, are as follows:
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.
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., 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.
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
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.
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
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