We propose a novel framework called IDEA that exploits electromagnetic (EM) side-channel signals to detect malicious activity on embedded and cyber-physical systems (CPS). IDEA first records EM emanations from an uncompromised reference device to establish a baseline of reference EM patterns. IDEA then monitors the target device's EM emanations. When the observed EM emanations deviate from the reference patterns, IDEA reports this as an anomalous or malicious activity. IDEA does not require any resource or infrastructure on, or any modification to, the monitored system itself. In fact, IDEA is isolated from the target device, and monitors the device without any physical contact. We evaluate IDEA by monitoring the target device while it is executing embedded applications with malicious code injections such as Distributed Denial of Service (DDoS), Ransomware and code modification. We further implement a control-flow hijack attack, an advanced persistent threat, and a firmware modification on three CPSs: an embedded medical device called SyringePump, an industrial Proportional-Integral-Derivative (PID) Controller, and a Robotic Arm, using a popular embedded system, Arduino UNO. The results demonstrate that IDEA can detect different attacks with excellent accuracy (AUC > 99.5%, and 100 percent detection with less than 1 percent false positives) from distances up to 3 m.

In previous work, we introduced zero-overhead profiling (ZOP), a technique that leverages the electromagnetic emissions generated by the computer hardware to profile a program without instrumenting it. Although effective, ZOP has several shortcomings: it requires test inputs that achieve extensive code coverage for its training phase; it predicts path profiles instead of complete execution traces; and its predictions can suffer unrecoverable accuracy losses. In this paper, we present zero-overhead path prediction (ZOP-2), an approach that extends ZOP and addresses its limitations. First, ZOP-2 achieves high coverage during training through progressive symbolic execution (PSE)-symbolic execution of increasingly small program fragments. Second, ZOP-2 predicts complete execution traces, rather than path profiles. Finally, ZOP-2 mitigates the problem of path mispredictions by using a stateless approach that can recover from prediction errors. We evaluated our approach on a set of benchmarks with promising results; for the cases considered, (1) ZOP-2 achieved over 90% path prediction accuracy, and (2) PSE covered feasible paths missed by traditional symbolic execution, thus boosting ZOP-2's accuracy.

Establishing trust for an execution environment is an important problem, and practical solutions for it rely on attestation, where an untrusted system (prover) computes a response to a challenge sent by the trusted system (verifier). The response typically is a checksum of the prover's program, which the verifier checks against expected values for a "clean" (trustworthy) system. The main challenge in attestation is that, in addition to checking the response, the verifier also needs to verify the integrity of the response computation. On higher-end processors, this integrity is verified cryptographically, using dedicated trusted hardware. On embedded systems, however, constraints prevent the use of such hardware support. Instead, a popular approach is to use the request-to-response time as a way to establish confidence. However, the overall request-to-response time provides only one coarse-grained measurement from which the integrity of the attestation is to be inferred, and even that is noisy because it includes the network latency and/or variations due to micro-architectural events. Thus, the attestation is vulnerable to attacks where the adversary has tampered with response computation, but the resulting additional computation time is small relative to the overall request-to-response time.