IDs alerts correlation using grammar-based approach

Intrusion Detection System (IDS) is a security technology that attempts to identify intrusions. Defending against multi-step intrusions which prepare for each other is a challenging task. In this paper, we propose a novel approach to alert post-processing and correlation, the Alerts Parser. Different from most other alert correlation methods, our approach treats the alerts as tokens and uses modified version of the LR parser to generate parse trees representing the scenarii in the alerts. An Attribute Context-Free Grammar (ACF-grammar) is used for representing the multi-step attacks. Attack scenarii information and prerequisites/consequences knowledge are included together in the ACF-grammar enhancing the correlation results. The modified LR parser depends on these ACF-grammars to generate parse trees. The experiments were performed on two different sets of network traffic traces, using different open-source and commercial IDS sensors. The discovered scenarii are represented by Correlation Graphs (CGs). The experimental results show that Alerts Parser can work in parallel, effectively correlate related alerts with low false correlation rate, uncover the attack strategies, and generate concise CGs.