DocumentCode :
3780044
Title :
Enhanced intrusion detection system based on bat algorithm-support vector machine
Author :
Adriana-Cristina Enache;Valentin Sg?rciu
Author_Institution :
Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, Bucharest, Romania
fYear :
2014
Firstpage :
1
Lastpage :
6
Abstract :
As new security intrusions arise so does the demand for viable intrusion detection systems These solutions must deal with huge data volumes, high speed network traffics and countervail new and various types of security threats. In this paper we combine existing technologies to construct an Anomaly based Intrusion Detection System. Our approach improves the Support Vector Machine classifier by exploiting the advantages of a new swarm intelligence algorithm inspired by the environment of microbats (Bat Algorithm). The main contribution of our paper is the novel feature selection model based on Binary Bat Algorithm with Levy flights. To test our model we use the NSL-KDD data set and empirically prove that Levy flights can upgrade the exploration of standard Binary Bat Algorithm. Furthermore, our approach succeeds to enhance the default SVM classifier and we obtain good performance measures in terms of accuracy (90.06%), attack detection rate (95.05%) and false alarm rate (4.4%) for unknown attacks.
Keywords :
"Support vector machines","Intrusion detection","Kernel","Particle swarm optimization","Algorithm design and analysis","Mathematical model"
Publisher :
ieee
Conference_Titel :
Security and Cryptography (SECRYPT), 2014 11th International Conference on
Type :
conf
Filename :
7509488
Link To Document :
بازگشت