Computer Engineering Department, Islamic Azad University, Babol Branch, Babol, Iran
Electrical and Computer Engineering Department, Nushirvani University of Technology, Babol, Iran
In recent years, the needs of the Internet are felt in lives of all people. Accordingly, many studies have been done on security in virtual environment. Old technics such as firewalls, authentication and encryption could not provide Internet security completely; So, Intrusion detection system is created as a new solution and a defense wall in cyber environment. Many studies were performed on different algorithms but the results show that using machine learning technics and swarm intelligence are very effective to reduce processing time and increase accuracy as well. In this paper, hybrid SVM and ABC algorithms has been suggested to select features to enhance network intrusion detection and increase the accuracy of results. In this research, data analysis was undertaken using KDDcup99. Such that best features are selected by Support vector machine, then selected features are replaced in the appropriate category based on artificial bee colony algorithm to reduce the search time, increase the amount of learning and improve the authenticity of intrusion detection. The results show that the proposed algorithm can detect intruders accurately on network up to 99.71%.