An alert fusion model inspired by artificial immune system. - Mahboubian, Mohammad; Udzir, Nur Izura; Subramaniam, Shamala; Abdul Hamid, Nor Asila Wati (2012)
In the recent years one of the most focused topics in the field of network security and more specifically intrusion detection systems was to find a solution to reduce the overwhelming alerts generated by IDSs in the network. Inspired by human defence system and danger theory we propose 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, our model managed to aggregate the alerts by the average rate of 97.5 percent.