A hybrid approach for IEEE 802.11 intrusion detection based on AIS, MAS and naïve Bayes

Many problems with wireless networks are directly related to the very means used to transport data, in this case, radio waves. In addition to mis-configured equipment lack of adaptable algorithms and wireless networks are major targets for attacks. New tools to refrain that are greatly in need. Due to the fact that it is easy to attack and not so to defend wireless networks, good candidate tools would be the ones that could profit from intelligent techniques. In this paper, we use the Danger Theory (DT) and a Bayesian classifier (using naive Bayes) embedded in a military style multi-agent system (MAS) to create a lightweight, adaptable and dynamic detection system for wireless networks (WIDS). Experimental results show that the artificial immune aspect of the proposed system is capable of detecting unknown intrusion and to identify them automatically with considerable few false alarms and low cost for the network traffic.