Unsupervised Anomaly Detection Based on System Logs
Hao Chen · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2021
The anomaly detection based on rich and descriptive system logs is critical to securing information systems.Existing techniques rarely consider semantic information of logs in the detection, resulting in their incapability to handle unseen log events, neither further improve their detection rates.This paper proposes a CNN and LSTM based anomaly detection approach.It utilizes the meaning of log entries -the semantic information of logs in the detection, where the relations among short sequences are automatically learned.The results of comparative experiments demonstrate the effectiveness of the proposed approach on both stable(fixed format) and unstable(unseen, unfixed format) logs.