DReAM: Deep Recursive Attentive Model for Anomaly Detection in Kernel Events

System logs and traces contain information that reflects the state of the system and serves as a rich source of knowledge for system monitoring from the application to the kernel layer. Moreover, logging of traces as a tool for monitoring the operation of a cyber-physical system is recommended by most safety standard organizations. However, because the data can be overwhelmingly huge within a short space of time, the use of models that do not rely only on known signatures for online anomaly detection becomes difficult to use due to the challenge of processing such an enormous amount of data at runtime. Hence, most practitioners resort to the use of signature-based tools. In this paper, we introduce an anomaly detection model that uses intra-trace and inter-trace context vectors with long short-term memory networks to overcome the challenge of online anomaly detection in cyber-physical systems. We test the performance of the model with publicly available datasets that reflect the internal and external control flow of an embedded application and our model demonstrates both the effectiveness and robustness in detecting an anomalous sequence in a system call stream.