International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
Transmission of data over the internet keeps on increasing. The need to protect connected systems also increasing. Intrusion Detection Systems (IDSs) are the latest technology used for this purpose. Datamining plays an important role in matching intrusions with the data stored in the system. Although the field of IDSs is still developing, the systems that do exist are still not complete, in the sense that they are not able to detect all types of intrusions. Some attacks which are detected by various tools available today cannot be detected by other products, depending on the types and methods that they are built on. In this research, an artificial neural network using back-propagation algorithm has been used to implement the IDS. Inspite of much related work had been done, this study elucidates the implementation aspects of BPA for a real_ time IDS.Thousand packet information both normal and intrusion have been considered for implementation. The result of ID is very close to 99%. The topology of the ANN is (41×10×1). The network converged with 550 iterations. Very huge amount of packets are to be evaluated to know the complete performance of the developed system.
How to cite this article:
E. Anbalagan , C. Puttamadappa , E. Mohan , B. Jayaraman and Srinivasarao Madane . Datamining and Intrusion Detection Using Back-Propagation Algorithm for Intrusion Detection.
DOI: https://doi.org/10.36478/ijscomp.2008.264.270
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2008.264.270