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Mining information from logs is an old and still active research topic. Inrecent years, with the rapid emerging of cloud computing, log mining becomesincreasingly important to industry. This paper focus on one major mission oflog mining: anomaly detection, and proposes a novel method for mining abnormalsequences from large logs. Different from previous anomaly detection systemswhich based on statistics, probabilities and Markov assumption, our approachmeasures the strangeness of a sequence using compression. It first trains agrammar about normal behaviors using grammar-based compression, then measuresthe information quantities and densities of questionable sequences according toincrementation of grammar length. We have applied our approach on mining somereal bugs from fine grained execution logs. We have also tested its ability onintrusion detection using some publicity available system call traces. Theexperiments show that our method successfully selects the strange sequenceswhich related to bugs or attacking.

Anomaly Sequences Detection from Logs Based on Compression