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Yoon, Man-Ki, Mohan, Sibin, Choi, Jaesik, Christodorescu, Mihai, Sha, Lui.  2017.  Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System. Proceedings of the Second International Conference on Internet-of-Things Design and Implementation. :191–196.

Existing techniques used for anomaly detection do not fully utilize the intrinsic properties of embedded devices. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. Our prototype applied to a real-world open-source embedded application shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths.