Multidimensional and Adaptive Non-Intrusive Anomaly Detection in Network Services
Kurnia Hendrawan, T. Sinnwell, Dirk Leinenbach · 2010
We propose the analysis of signal-based metrics with a combination of wavelet transform and Mahalanobis dis- tance to automatically detect anomalies in network services. In contrast to conventional detection methods like thresholding, our technique adapts automatically to gradual changes in the measured signals and deals well with periodical load patterns. It supports multidimensional analysis to improve reliability and significance of the detection and provides confidence values, which are the base for judging the anomaly. Keywords—Anomaly detection, Mahalanobis distance, net- works, wavelet