Publications du laboratoire
|A Multiple Classifier System Using an Adaptive Strategy for Intrusion Detection |
Auteur(s): BAHRI E., HARBI N., NGUYEN H.-h.
Actes de conférence: Conference: International Conference on Intelligent Computational System (ICICS'2012) (Dubai, AE, 2012-01-07) Publié: International Conference on Intelligent Computational System (ICICS'2012), vol. (2012) p.124-128
Résumé: Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious network-based or host-based attacks by using traditional statistical methods to new data mining approaches in last decades. In effect, the detection of the anomalies in the data-processing networks is regarded as one problem of data classification where the use of the data mining techniques and machine learning. In this paper, we present a new method performing the intrusion detection system. This approach, called MCSAS, is based on a multiple classifier System that uses an adaptive strategy for intrusion detection. The adaptive strategy is inspired from Boosting, an ensemble method that distinguishes attacks from normal behaviors and identifies different types of intrusions. The experimental results, conducted on the KDD99 dataset, prove that our proposed solution approach outperforms several state-of-the-art methods, particularly indetecting rare attack types.