An improved Genetic Optimized Neural Network for Multimodal Biometrics

23-30In this paper, a novel classification technique for multimodal biometric system based on fingerprint and palmprint is proposed. The problems faced in unimodal biometric system such as noisy data, intra class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates are overcome in multimodal biometric system by integrating the evidence presented by multiple traits. It is proposed to fuse the features of the fingerprint with palmprint images. Features are extracted using Gabor filter and Discrete Cosine Transform (DCT). The extracted feature vectors were classified using an improved Partial Recurrent Neural Network with genetic optimization. The proposed Momentum Optimized Genetic Partial Recurrent Neural Network (MOG-PRNN) was evaluated using a publicly available dataset and features obtained from live dataset. The experimental results obtained show an average classification accuracy of 98.6% with different datasets.</span

Cross –Layer Intrusion Detection System for Wireless Sensor Networks

213-220In Wireless Sensor Network, the sensor nodes monitor any abnormal events occurring around the business environment to report the emergency alert whenever it detects any abnormality in an environment or report the monitored data continuously or periodically to the base station. These emergency alerts can be stolen or modified by the attackers or in some cases, the sensor nodes can be physically compromised by the attackers, which lead to an unsafe environment. To avoid these problems, Intrusion Detection System is implemented at the base station to filter the abnormal data mostly related to the attacks of network layer. But this paper proposes a novel cross layer rule based intrusion detection system to detect the attacks coming from different layers in Wireless Sensor Networks. The method analyzed several detection rules for Physical, MAC, Network and Application layer attacks. The implementation is done using the rules identified from the IDS techniques available for Wireless Sensor Network. The experimental result shows the detection rate of different attacks on different layers. The performance of cross layer IDS are shown in the graph by making the comparison between the detection rates of various IDS techniques

Contactless Palm Vein Authentication security technique for better adoption of e-commerce in developing countries

E-commerce has been contributing immensely to the economic development of the developed countries and the main catalyst to this could be attributed to the total adoption of e-commerce by the citizens. In order word, e-commerce could also be an economic driver in developing countries. Moreover, security has been identified as major barrier that prevents citizens from adopting e-commerce in developing countries. This paper examines Security Authentication Techniques (SAT) of Digital Signature (DF) and Fingerprint System (FPS) the limitations of these architectures and then propose Contactless Palm Vein Authentication (CPVA). The architecture of this new CPVA will be discussed in relation to Security, privacy, trust and reliability