Hybrid fuzzy-PSO based approach to detect intrusion in networks

Abstract

In Internet-based communication, various types of attacks have been evolved. Hence, attacker easily breaches the securities. Traditional techniques of intrusion detection have failed to observe these attacks and thus hefty systems are required to remove these attacks before uncovering of the entire network by attacks. The power of artificial intelligence systems is used, to make high computational speed, boost fault tolerance and error resistance against turbulent data. Therefore, fuzzy-PSO (particle swarm optimization) based hybrid approach is designed. The fuzzy logic based on the degree of truth whereas PSO algorithm depends on stochastic technique that helps in learning from the scenario, thus their combination increases the toughness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal as well as anomalism behavior in the network. DARPA-KDD99 dataset examined on this system to address the behavior of each connection on the network and compared with an existing system. This approach improves the result on the aspects of precision, recall, and F1-score.