Title: AN AIS INSPIRED ALERT REDUCTION MODEL

Issue Number: Vol. 1, No. 2
Year of Publication: Oct - 2012
Page Numbers: 130-139
Authors: Mohammad Mahboubian, Nur Izura Udzir, Shamala Subramaniam, Nor Asila Wati Abdul Hamid
Journal Name: International Journal of Cyber-Security and Digital Forensics (IJCSDF)
- Hong Kong

Abstract:


One of the most important topics in the field of intrusion detection systems is to find a solution to reduce the overwhelming alerts generated by IDSs in the network. Inspired by danger theory which is one of the most important theories in artificial immune system (AIS) we proposed a complementary subsystem for IDS which can be integrated into any existing IDS models to aggregate the alerts in order to reduce them, and subsequently reduce false alarms among the alerts. After evaluation using different datasets and attack scenarios and also different set of rules, in best case our model managed to aggregate the alerts by the average rate of 97.5 percent.