Visible to the public Biblio

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Yoon, Man-Ki, Liu, Bo, Hovakimyan, Naira, Sha, Lui.  2017.  VirtualDrone: Virtual Sensing, Actuation, and Communication for Attack-resilient Unmanned Aerial Systems. Proceedings of the 8th International Conference on Cyber-Physical Systems. :143–154.

As modern unmanned aerial systems (UAS) continue to expand the frontiers of automation, new challenges to security and thus its safety are emerging. It is now difficult to completely secure modern UAS platforms due to their openness and increasing complexity. We present the VirtualDrone Framework, a software architecture that enables an attack-resilient control of modern UAS. It allows the system to operate with potentially untrustworthy software environment by virtualizing the sensors, actuators, and communication channels. The framework provides mechanisms to monitor physical and logical system behaviors and to detect security and safety violations. Upon detection of such an event, the framework switches to a trusted control mode in order to override malicious system state and to prevent potential safety violations. We built a prototype quadcoper running an embedded multicore processor that features a hardware-assisted virtualization technology. We present extensive experimental study and implementation details, and demonstrate how the framework can ensure the robustness of the UAS in the presence of security breaches.

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.