Context-Awareness to improve Anomaly Detection in Dynamic Service Oriented Architectures

Revealing anomalies to support error detection in software-intensive systems is a promising approach when traditional detection mechanisms are considered inadequate or not applicable. The core of anomaly detection lies in the definition of the expected behavior of the observed system. Unfortunately, the behavior of complex and dynamic systems is particularly difficult to understand. To improve the accuracy of anomaly detection in such systems, in this paper we present a context-aware anomaly detection framework which acquires information on the running services to calibrate the anomaly detection. To cope with system dynamicity, our framework avoids instrumenting probes into the applica-tion layer of the observed system monitoring multiple underlying layers instead. Experimental evaluation shows that the detection accuracy is in-creased considerably through context-awareness and multiple layers moni-toring. Results are compared to state-of-the-art anomaly detectors exer-cised in demanding more static contexts.