Search results for: V. Venkatachalam

Authors: Natarajan Venkatachalam, Subrahmanya V. R. K. Rao, Vijay Karthikeyan Dhandapani, Swaminathan Saravanavel

Abstract:

The quantum communication technology is an evolving design which connects multiple quantum enabled devices to internet for secret communication or sensitive information exchange. In future, the number of these compact quantum enabled devices will increase immensely making them an integral part of present communication systems. Therefore, safety and security of such devices is also a major concern for us. To ensure the customer sensitive information will not be eavesdropped or deciphered, we need a strong authentications and encryption mechanism. In this paper, we propose a mutual authentication scheme between these smart quantum devices and server based on the secure exchange of information through quantum channel which gives better solutions for symmetric key exchange issues. An important part of this work is to propose a secure mutual authentication protocol over the quantum channel. We show that our approach offers robust authentication protocol and further our solution is lightweight, scalable, cost-effective with optimized computational processing overheads.

Keywords: Quantum cryptography, quantum key distribution, wireless quantum communication, authentication protocol, quantum enabled device, trusted third party.

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Authors: V. Venkatachalam, S. Selvan

Abstract:

The security of computer networks plays a strategic role in modern computer systems. Intrusion Detection Systems (IDS) act as the 'second line of defense' placed inside a protected network, looking for known or potential threats in network traffic and/or audit data recorded by hosts. We developed an Intrusion Detection System using LAMSTAR neural network to learn patterns of normal and intrusive activities, to classify observed system activities and compared the performance of LAMSTAR IDS with other classification techniques using 5 classes of KDDCup99 data. LAMSAR IDS gives better performance at the cost of high Computational complexity, Training time and Testing time, when compared to other classification techniques (Binary Tree classifier, RBF classifier, Gaussian Mixture classifier). we further reduced the Computational Complexity of LAMSTAR IDS by reducing the dimension of the data using principal component analysis which in turn reduces the training and testing time with almost the same performance.

Keywords: Binary Tree Classifier, Gaussian Mixture, IntrusionDetection System, LAMSTAR, Radial Basis Function.

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