DISTRIBUTED ANOMALY INTRUSION DETECTION SYSTEM BASED ON MULTI-AGENTS

Abstract

Networks have the problem of security attacks like denial of service attacks and others. The firewalls and encrypted software’s does not provide a complete security solution for those attacks. Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this paper, we have proposed an effective Intrusion Detection System in which local agent collects data from its own system and it classifies anomaly behaviors using SVM classifier. Each local agent is capable of removing the host system from the network on successful detection of attacks. The mobile agent gathers information from the local agent before it allows the system to send data. Our system identifies successful attacks from the anomaly behaviors. Experimental results show that the proposed system has high detection rate and low false alarm rate which encourages the proposed system.