International Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/157

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Author: search.filters.author.N, RadhaEnd date: 2019

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    FINGERPRINT BIO-CRYPTO KEY GENERATION USING SCALE INVARIANT FEATURE TRANSFORM (SIFT)
    (International Research Journal of engineering and technology(IJERT), 2016-11) R, Partheeba; N, Radha
    Network security has become a great threat to the network accessible resources that consists of policies to prevent, monitor unauthorized access, modification, and misuse of computer network. Several algorithms and techniques were proposed for the secure transmission of data and to protect user’s privacy. Secret-key cryptography and public-key cryptography are the techniques used for the protection of security issues. However, such a key need to be stored in a protected place or it should be transported by a shared communication line. So, generation of cryptographic key using biometric traits of both sender and receiver during communication avoids key storing and improves security strength. The proposed approach for detecting the quality of fingerprint by using the method called orientation certainty level (OCL). If the image has good quality then feature extraction will be done using Scale Invariant Feature Transform, otherwise poor-quality image will get ignored. By using cover image, the obtained cancellable template will get hidden. Then the hidden image will be transmitted from sender to receiver and receiver to receiver to sender by using Variable Least Significant Bit techniques. Finally, the performance metrics like FAR (False Acceptance Rate), FRR (False Rejection Rate), and Accuracy of the proposed work is compared with the existing system.
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    A STUDY ON BIOMETRIC TEMPLATE SECURITY
    (ICTACT Journal on Soft Computing, 2010-01) N, Radha; S, Karthikeyan
    The increasing popularity of biometrics and cryptography is driven by the widespread stipulation on information security. Abundant efforts have been made in developing successful methods in these areas in order to accomplish an enhanced level of information security. There are two dominant issues in information security enhancement. One is to defend the user ownership and control the access to information by authenticating an individual’s identity. The other is to make sure the privacy and integrity of information and to secure communication. Cryptography is the science of writing in secret code. Secret-key cryptography and public-key cryptography are the two most important cryptographic architectures. The security of a cryptographic system is reliant on the secrecy of the cryptographic key. Biometric authentication or simply biometrics refers to establishing automatic personal recognition based on the physical and behavioral characteristics of an individual (e.g. face, voice, fingerprint, gait, hand geometry, iris, gene, etc.). Biometrics offers superior security and easier than traditional identity authentication systems (based on passwords and cryptographic keys).Since biometrics characteristics are naturally related with a particular individual, making them insusceptible to being stolen, forgotten, lost or attached. This paper presents a survey on various techniques proposed earlier in developing an authentication system for ensuring individual’s information security by combining biometric characteristics of that particular individual and the cryptographic techniques. In addition, it provides some fundamental idea for future research that may help in eliminating the problems associated with the present authentication systems
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    SECURING RETINA FUZZY VAULT SYSTEM USING SOFT BIOMETRICS
    (Global Journal of Computer Science and Technology, 2010-09) N, Radha; S, Karthikeyan; P, Anupriya
    The major concern of almost all the verification system is user authentication and security. This necessitates the development of a mechanism that ensures user security and privacy. A lot of research has been carried on this developing field and numerous techniques have been proposed earlier in literature. These traditional methods use tokens and passwords to provide security to the users. Uncertainly, it can be easily compromised by attackers and therefore it is significant to design verification system that ensures authentication. In recent years, technology has turned in favor of combining soft biometrics and cryptographic key generation technique. The principal feature of using soft biometric template is that it cannot be easily revoked by any unauthorized user. Most commonly used soft biometric features are iris, retina, face, fingerprint, voice and so on. Fuzzy vault is the framework which comprises of the combination of soft biometrics and cryptographic key generation technique. This fuzzy vault acts as an additional layer of security. This overcomes the limitation met by a biometrics system when implemented individually. This paper proposes a biometric verification system investigating the combined usage of soft biometrics features hardened by fuzzy vault scheme. This approach uses retina as a soft biometric since it is capable of providing best results. Experiments were conducted to investigate the performance of the proposed authentication system in ensuring the user security and privacy.
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    ANALYZING DATAMINING ALGORITHMS USING CAR DATASET
    (CiiT International Journal of Data Mining and Knowledge Engineering, 2009-09) R, Deepalakshmi; N, Radha
    The “Car Manufacturing” sector occupies a prime position in the development of automobile industry. In this paper, a proposed data mining application in car manufacturing domain is explained and experimented. The datasets are retrieved from UCI Machine learning repository. The purpose of this paper is to establish a classifier that is much more reliable in classifications for future objects. The classifier should provide sophisticated prediction to indicate the car data for a new input instance with some attributes, such as car type, body-style, horsepower and fuel. Such analysis helps in providing car market with base for more accurate result for the future market. The physical characteristics of a car viz. aspiration, number of doors, body-style, normalized losses, car-type, drive wheels, engine-location, wheel-base, curb-weight, horse-power, bore, stroke, city-mpg, highway-mpg, price, engine size, etc., are considered to determine the performance of a car. Hence development of such a classifier, though a voluminous task, is immensely essential in car manufacturing realm. Machine learning techniques can help in the integration of computer-based systems in predicting the quality of car and to improve the efficiency of the system. The classification models were trained by using 214 datasets. The predicted values for the classifiers were evaluated using 10-fold cross validation and the results were compared.