Implementation of intrusion detection system using artificial bee colony with correlation-based feature selection

Abstract

In any organization, information can be transmitted over the Internet. Maintaining confidentiality of the data during transmission plays a vital role. Many existing approaches like firewalls, antivirus, encryption and decryption techniques are available to provide security. But still these approaches suffer due to the sophisticated nature of the attackers. So we are moving towards swarm intelligence approaches to build intrusion detection system. In this paper, we use a swarm intelligence approach namely the artificial bee colony to implement classifier. It will generate classification rules to detect the intruder. For this KDD dataset was used. Before classification rule generation subsets were generated depending on the correlation existing between attributes and target class label to reduce complexity. The results show that ABC effectively identified the different types of attacks compared to the existing ones.