We propose using out-of-band emanations from embedded devices in order to detect malicious code execution. We passively monitor involuntary electromagnetic (EM) emissions from embedded devices to find and detect new signals. We demonstrate the efficacy and feasibility of an EM emanation based anomaly detection system using commercial off-the-shelf (COTS) software defined radio (SDR) hardware to detect code execution on an industrial control system (the Allen-Bradley 1756-EWEB module). We have developed a fully automated training and testing framework for this anomaly detection system. In this paper, we describe the system architecture, the cliff-detection algorithm used to process the received emanations, the testing setup and procedures, and our results. When trained on one set of EWEB modules and tested on a separate set, we present an experimental prototype capable of detecting unknown (attack) code execution with 98% accuracy at 100% detection rate. We present data supporting the robustness of these results across 16 physical device instances and with training recordings taken months apart from testing recordings.
Nathaniel Boggs, Jimmy C. Chau, and Ang Cui, "Utilizing electromagnetic emanations for out-of-band detection of unknown attack code in a programmable logic controller," Proc. SPIE 10630, Cyber Sensing 2018, 106300D (Presented at SPIE Defense + Security: April 17, 2018; Published: 3 May 2018); https://doi.org/10.1117/12.2304465.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the proceedings. They include the speaker's narration with video of the slides and animations. Most include full-text papers. Interactive, searchable transcripts and closed captioning are now available for 2018 presentations, with transcripts for prior recordings added daily.
Search our growing collection of more than 16,000 conference presentations, including many plenaries and keynotes.