Using Artificial Immune System and Fuzzy Logic for Alert Correlation

One of the most important challenges facing the intrusion detection systems (IDSs) is the huge number of generated alerts. A system administrator will be overwhelmed by these alerts in such a way that she/he cannot manage and use the alerts. The best-known solution is to correlate low-level alerts into a higher level attack and then produce a high-level alert for them. In this paper a new automated alert correlation approach is presented. It employs Fuzzy Logic and Artificial Immune System (AIS) to discover and learn the degree of correlation between two alerts and uses this knowledge to extract the attack scenarios. The proposed system doesn’t need vast domain knowledge or rule definition efforts. To correlate each new alert with previous alerts, the system first tries to find the correlation probability based on its fuzzy rules. Then, if there is no matching rule with the required matching threshold, it uses the AIRS algorithm. The system is evaluated using DARPA 2000 dataset and a netForensics honeynet data. The completeness, soundness and false alert rate are calculated. The average completeness for LLDoS1.0 and LLDoS2.0, are 0.957 and 0.745 respectively. The system generates the attack graphs with an acceptable accuracy and, the computational complexity of the probability assignment algorithm is linear.