Alert Correlation Approach Based on HMM

Huge amounts of low level alerts are daily reported by IDS.Particularly, the large number of which are false positives.A very large quantity of false positives alerts makes it difficult for the security manager to analysis them; thus, in order to cope with such quantities of alerts, alert correlation approaches have been used.In this paper, we describe an architecture for alert correlation based on Hidden Markov Model. Our aim is to reduce the alert redundancy and an extract attack scenario among alerts.An aggregation and correlation module are vital modules of our method. The aggregation module is used to combine the same alerts together. The outcomes of this module are hyper alerts. Afterward, hyper alerts are mapped to the states of the HMM, then the correlation engine estimates the correlation between two hyper alerts among the states. Finally, in order to discover an attack scenario we considered the correlation value between two hyper alerts , so an attack scenario is illustrated by the graph of nodes and edges. The most important point of our approach is that attack scenarios can be detected online without expert knowledge. The efficiency of our proposed approach is evaluated using both the DARPA 2000 dataset and the live traffic data collected from a Honey net network. The experimental results show that the correlation model is effective in achieving alert reduction and discovering the attack scenario.

کلیدواژه ها:

Alert Correlation ، Network Security ، Attack scenario ، Intrusion Detection Systems ، Hidden Markov Model