S.Selvakani Kandeeban

Work place: Department of Computer Applications, Francis Xavier Engineering College, Tirunelveli, Tamilnadu, India

E-mail: sselvakani@hotmail.com


Research Interests: Computer systems and computational processes, Information Security, Network Security, Data Structures and Algorithms, Information-Theoretic Security


Dr. S. Selvakani received the MCA degree from Manonmanium Sundaranar University and M.Phil degree from Madurai Kamaraj University. She received Her research interest includes Network
Security and Soft computing. She has presented 8 papers in National Conference and 1 paper in international conference. She has published 3 paper in National journal and 11 papers in International Journals. She is currently pursuing her Ph.D degree in Network Security under the Guidance of Dr. R.S.Rajesh. Presently she is working as a Professor and Head, MCA Dept in Francis Xavier Engineering College, Tirunelveli, India.

Author Articles
A Frame of Intrusion Detection Learning System Utilizing Radial Basis Function

By S.Selvakani Kandeeban R.S.Rajesh

DOI: https://doi.org/10.5815/ijmecs.2012.01.03, Pub. Date: 8 Jan. 2012

The process of monitoring the events that occur in a computer system or network and analyzing them for signs of intrusion is known as Intrusion Detection System (IDS). Detection ability of most of the IDS are limited to known attack patterns; hence new signatures for novel attacks can be troublesome, time consuming and has high false alarm rate. To achieve this, system was trained and tested with known and unknown patterns with the help of Radial Basis Functions (RBF). KDD 99 IDE (Knowledge Discovery in Databases Intrusion Detection Evaluation) data set was used for training and testing. The IDS is supposed to distinguish normal traffic from intrusions and to classify them into four classes: DoS, probe, R2L and U2R. The dataset is quite unbalanced, with 79% of the traffic belonging to the DoS category, 19% is normal traffic and less than 2% constitute the other three categories. The usefulness of the data set used for experimental evaluation has been demonstrated. The different metrics available for the evaluation of IDS were also introduced. Experimental evaluations were shown that the proposed methods were having the capacity of detecting a significant percentage of rate and new attacks.

[...] Read more.
Other Articles