An AI-Enabled Side Channel Power Analysis Based Hardware Trojan Detection Method for Securing the Integrated Circuits in Cyber-Physical Systems

TL;DR

This paper introduces an AI-based side-channel power analysis method for detecting hardware Trojans in integrated circuits, enhancing security in cyber-physical systems by non-invasively identifying malicious modifications.

Contribution

It presents a novel AI-enabled approach utilizing power signal features for hardware Trojan detection, outperforming existing baseline methods on a standard dataset.

Findings

Higher detection accuracy than baseline models

Effective use of time-domain and frequency-domain features

Non-invasive detection method

Abstract

Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). ICs are largely susceptible to various hardware attacks with malicious intents. One of the stealthiest threats is the insertion of a hardware trojan into the IC, causing the circuit to malfunction or leak sensitive information. Due to supply chain vulnerabilities, ICs face risks of trojan insertion during various design and fabrication stages. These trojans typically remain inactive until triggered. Once triggered, trojans can severely compromise system safety and security. This paper presents a non-invasive method for hardware trojan detection based on side-channel power analysis. We utilize the dynamic power measurements for twelve hardware trojans from IEEE DataPort. Our approach applies to signal processing techniques to extract crucial time-domain and frequency-domain features from the power traces, which are then used for trojan detection leveraging Artificial Intelligence (AI) models. Comparison with a baseline detection approach indicates that our approach achieves higher detection accuracy than the baseline models used on the same side-channel power dataset.