Biblio

In internet based communication, various types of attacks have been evolved. Hence, attacker easily breaches the securities. Traditional intrusion detection techniques to observe these attacks have failed and thus hefty systems are required to remove these attacks before they expose entire network. With the ability of artificial intelligence systems to adapt high computational speed, boost fault tolerance, and error resilience against noisy information, a hybrid particle swarm optimization(PSO) fuzzy rule based inference engine has been designed in this paper. The fuzzy logic based on degree of truth while the PSO algorithm based on population stochastic technique helps in learning from the scenario, thus their combination will increase the toughness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal as well as anomalism behaviour in the network. DARPA-KDD99 dataset examined on this system to address the behaviour of each connection on network and compared with existing system. This approach improves the result on the basis of precision, recall and F1-score.