Discovering Novel Multistage Attack Strategies

Abstract

In monitoring anomalous network activities, intrusion detection systems tend to generate a large amount of alerts, which greatly increase the workload of post-detection analysis and decision-making. A system to detect the ongoing attacks and predict the upcoming next step of a multistage attack in alert streams by using known attack patterns can effectively solve this problem. The complete, correct and up to date pattern rule of various network attack activities plays an important role in such a system. An approach based on sequential pattern mining technique to discover multistage attack activity patterns is efficient to reduce the labor to construct pattern rules. But in a dynamic network environment where novel attack strategies appear continuously, the novel approach that we propose to use incremental mining algorithm shows better capability to detect recently appeared attack. In order to improve the correctness of results and shorten the running time of the mining algorithms, the directed graph is presented to restrict the scope of data queried in mining phase, which is especially useful in incremental mining. Finally, we remove the unexpected results from mining by computing probabilistic score between successive steps in a multistage attack pattern. A series of experiments show the validity of the methods in this paper.