A Futuristic Analysis Approach of Neural Network for Intrusion Detection System.
Abstract
Security is a crucial issue for computer systems; so, Intrusion Detection Systems began to exist as the next level of security. Artificial intelligence techniques provide a conceivable solution to intrusion detection. In this document, a qualitative approach of neural networks is proposed to detect intrusions based on analysis of relevant characteristics. The results show that separate neural networks based on the analysis of relevant characteristics for each type of attack detect attacks faster than the unique neural network classifier with the data set we have used from the KDD Cup data. The neural network approach reduces training time and attacks can be detected over time.
References
Cannady, J (1998). Applying Neural Networks for Misuse Detection”, Proc. 21st National Information Systems Security Conference, pp. 368-381, 1998.
Idris, D., Shanmugam, N.B (2005). Artificial Intelligence Techniques applied to Intrusion Detection, IEEE Indicon 2005 Conference, Chennai, India, pp. 11-13, (2005).
KDD CUP Data, (1999). The Fifth International Conference on Knowledge Discovery and Data mining, 1999.
Khorramabad, Iran. (2015). Implementation of Support Vector Machines and Clustering of Intrusion Detection System for Computer Networks.
Mehdi, M., Mohammad, Z. (2004). A Neural Network Based System for Intrusion Detection and Classification of Attacks, Proc. of 2004 IEEE International Conference on Advances in Intelligent Systems - Theory and Applications, Luxembourg-Kirchberg, Luxembourg, November 15-18, 2004.
Susan C. L., David V. H, (2001). Training a Neural-Network Based Intrusion Detector to Recognize Novel Attacks”, IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, Vol. 31, No. 4, pp. 294-299, July 2001.
