Research on discovering multi-step attack patterns based on clustering IDS alert sequences - MEI Hai-bin1; GONG Jian1; ZHANG Ming-hua2 (Tongxin xuebao, 2011)
A method of discovering multi-step attack patterns from alert data was studied.Alert similarity function was defined to construct the set of attack activity sequences.Sequence alignment technology was used to cluster the similar attack activity sequences.Multi-step attack patterns in a cluster were automatically discovered by the longest common subsequence extraction algorithm based on the idea of dynamic programming.The proposed method didn’t depend on large amounts of prior knowledge.Few configuration parameters were needed and it was easy to implement.Experimental results demonstrate the effectiveness of proposed method.