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Cyber-Physical Systems (CPS) play a significant role in our criticalinfrastructure networks from power-distribution to utility networks. Theemerging smart-grid concept is a compelling critical CPS infrastructure thatrelies on two-way communications between smart devices to increase efficiency,enhance reliability, and reduce costs. However, compromised devices in thesmart grid poses several security challenges. Consequences of propagating fakedata or stealing sensitive smart grid information via compromised devices arecostly. Hence, early behavioral detection of compromised devices is criticalfor protecting the smart grid's components and data. To address these concerns,in this paper, we introduce a novel and configurable system-level framework toidentify compromised smart grid devices. The framework combines system andfunction call tracing techniques with signal processing and statisticalanalysis to detect compromised devices based on their behavioralcharacteristics. We measure the efficacy of our framework with a realisticsmart grid substation testbed that includes both resource-limited andresource-rich devices. In total, using our framework, we analyze six differenttypes of compromised device scenarios with different resources and attackpayloads. To the best of our knowledge, the proposed framework is the first indetecting compromised CPS smart grid devices with system and function-levelcall tracing techniques. The experimental results reveal an excellent rate forthe detection of compromised devices. Specifically, performance metrics includeaccuracy values between 95% and 99% for the different attack scenarios.Finally, the performance analysis demonstrates that the use of the proposedframework has minimal overhead on the smart grid devices' computing resources.

A System-level Behavioral Detection Framework for Compromised CPS Devices: Smart-Grid Case