A Computational Harmonic Detection Algorithm to Detect Data Leakage through EM Emanation

TL;DR

This paper introduces a computational harmonic detection algorithm that effectively identifies EM emanations from various electronic devices, significantly improving data leakage detection accuracy over previous CNN-based methods and demonstrating practical applicability.

Contribution

The paper presents a novel harmonic-based detection algorithm that outperforms prior CNN methods, achieving near 100% accuracy across multiple device types and environments.

Findings

Achieves ~100% detection accuracy for EM emanations.

Outperforms previous CNN-based methods (~95% accuracy).

Proven effective in diverse real-world environments.

Abstract

Unintended electromagnetic emissions, called EM emanations, can be exploited to recover sensitive information, posing security risks. Metal shielding, used by defense organizations to prevent data leakage, is costly and impractical for widespread use. This issue is particularly significant for IoT devices due to their sheer volume and varied deployment environments. Therefore, there is a research need for an automated detection method to monitor facilities and address data leakage promptly. To resolve this challenge, in the preliminary version of this work [1], a CNN-based detection method was proposed using HDMI cable emanations that provided ~95% accuracy up to 22.5 m but had limitations due to training data. In this extended version, we augment the initial study by collecting and characterizing emanation data from IoT devices, everyday electronics, and cables. We propose a harmonic-based emanation detection method by developing a computational harmonic detection algorithm. The proposed method addresses the limitations of the CNN-based method and provides ~100% accuracy not only for HDMI emanation (compared to ~95% in the earlier CNN method) but also for all other tested devices and cables. Finally, it has also been tested in different environments to prove its efficacy in practical scenarios.