Hybrid Security Framework for Activity Based Authentication using RSA & Genetic Algorithm

In the current information age, security has achieved a tremendous importance in e-commerce applications involving financial transactions. Non-repudiation, data integrity, data confidentiality and authenticity, have become an integral part of information security. There is a tremendous risk involved in the communication of a plain text over Internet. Cryptography offers a solution for this type of risk which is referred to as a technique of encrypting and decrypting messages in such a way that they cannot be interpreted by anybody with the exception of a sender and an intended recipient. In majority of the e-commerce based applications where security is considered to be of prime importance, a single encryption algorithm is adopted for encrypting a password and the authentication information is stored on a single database server which becomes open to risks against different computer hacks. A novel solution for this problem is to generate an individual’s personal and dynamic activities which will be hard for the attackers to guess. Further, this can be combined with distributed technology where the authentication information is distributed over geographically separated multiple servers. In this paper authors have generated an activity based distributed 3D password incorporating various activities where the authentication information is distributed over geographically separated multiple authentication servers. The key pair is generated using RSA algorithm which is encrypted using single-point cross over and mutation of bits at the extreme position. This further adds another level of security and renders the key unbreakable by an unintended user. The configuration information pertaining to the distributed environment is stored in XML file which is parsed using Microsoft's XML Parser and the activity related information is stored in different servers which is encrypted using RSA algorithm. The technique employed combines RSA algorithm with Genetic Algorithm to offer a robust hybrid security framework in a distributed environment which is difficult to guess for an unintended user

Real time faces localisation and recognition with a RBF neural network: Algorithm and architecture

This paper describes a real time vision system, allowing to localize faces in video sequences and to recognize their identity. These processes are based on combining techniques of image processing and neural network approach. The robustness of this system has been evaluated quantitatively on 8 video sequences. We have tested our model using the ORL database in order to compare performances with other systems. The system has also been implanted on electronic architectures based on dedicated chip ZISC and FPGA. We analyse the algorithm complexity and we present results of hardware implementations in terms of used resources and processing speed.Cet article décrit un système de vision temps réel permettant de localiser des visages dans des séquences vidéo ainsi que de reconnaître leur identité. Ces processus sont effectués en combinant des techniques de traitements d'images et des méthodes de réseaux de neurones. La robustesse du système a été évaluée quantitativement sur un corpus de 8 séquences vidéo. Dans le but de comparer les performances avec les autres méthodes existantes, nous avons également testé notre modèle en utilisant la banque de visages standard ORL. Le système a aussi été implanté sur deux architectures électroniques à base de composants spécialisés ZISC et de FPGA. Nous analysons la complexité de l'algorithme et nous présentons les résultats des implantations architecturales en termes de ressources matérielles et de vitesse de traitement

Face Recognition using a Hybrid Supervised/Unsupervised Neural Network

A system for automatic face recognition is presented. It consists of several steps; Automatic detection of the eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by a hybrid (supervised and unsupervised) Neural Network. Two methods for reducing the overfitting -- a common problem in high dimensional classification schemes -- are presented, and the superiority of their combination is demonstrated. Key words: Face recognition, Neural Networks, Interest points, Symmetry operator. To appear: Pattern Recognition Letters 17 (1996) 67-76 1 Introduction Automatic face recognition has gained much attention in recent years, due to the variety of potential applications, and the increase in computational power which enables effective implementation of algorithms. Traditionally, face recognition was based on extracting certain features (e.g. spatial location of facial features and their geometrical relations) [4, 20]...