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.

The goal of this paper is to answer how much information is “transmitted” by the execution of particular sequence of instructions in a processor. Introducing such a measure would provide quantitative guidance for designing programs and computer hardware that minimizes inadvertent (side channel) information leakage, and would also help detect parts of a program or hardware design that have unusually high leakage (i.e., were designed to function as covert channel “transmitters”). To answer this question, we propose a new method to estimate the maximum information leakage through EM signals generated by the execution of instructions in a processor. We start by deriving a mathematical relationship between electromagnetic side-channel energy of individual instructions and the measured pairwise side-channel signal power. Then, we use this measure to calculate the transition probabilities needed for estimating capacity. Finally, we propose a new method to estimate side/covert channel capacity created by the execution of instructions in a processor and illustrate our results in several computer systems.

This paper presents an approach for zero-overhead profiling (ZOP). ZOP accomplishes accurate program profiling with no modification to the program or system during profiling and no dedicated hardware features. To do so, ZOP records the electromagnetic (EM) emanations generated by computing systems during program execution and analyzes the recorded emanations to track a program’s execution path and generate profiling information. Our approach consists of two main phases. In the training phase, ZOP instruments the program and runs it against a set of inputs to collect path timing information while simultaneously collecting waveforms for the EM emanations generated by the program. In the profiling phase, ZOP runs the original (i.e., uninstrumented and unmodified) program against inputs whose executions need to be profiled, records the waveforms produced by the program, and matches these waveforms with those collected during training to predict which parts of the code were exercised by the inputs and how often. We evaluated an implementation of ZOP on several benchmarks and our results show that ZOP can predict path profiling information for these benchmarks with greater than 94% accuracy on average.

While all computation generates electromagnetic (EM) side-channel signals, some of the strongest and farthest-propagating signals are created when an existing strong periodic signal (e.g. a clock signal) becomes stronger or weaker (amplitude-modulated) depending on processor or memory activity. However, modern systems create emanations at thousands of different frequencies, so it is a difficult, error-prone, and time-consuming task to find those few emanations that are AM-modulated by processor/memory activity.

This paper presents a new metric, which we call Signal Available to Attacker (SAVAT), that measures the side channel signal created by a specific single-instruction difference in program execution, i.e. The amount of signal made available to a potential attacker who wishes to decide whether the program has executed instruction/event A or instruction/event B. We also devise a practical methodology for measuring SAVAT in real systems using only user-level access permissions and common measurement equipment. Finally, we perform a case study where we measure electromagnetic (EM) emanations SAVAT among 11 different instructions for three different laptop systems. Our findings from these experiments confirm key intuitive expectations, e.g. That SAVAT between on-chip instructions and off-chip memory accesses tends to be higher than between two on-chip instructions. However, we find that particular instructions, such as integer divide, have much higher SAVAT than other instructions in the same general category (integer arithmetic), and that last-level-cache hits and misses have similar (high) SAVAT. Overall, we confirm that our new metric and methodology can help discover the most vulnerable aspects of a processor architecture or a program, and thus inform decision-making about how to best manage the overall side channel vulnerability of a processor, a program, or a system.