PCB Hardware Trojan Run-time Detection Through Machine Learning



Piliposyan, Gor ORCID: 0000-0001-7182-0717 and Khursheed, Syed-Saqib
(2022) PCB Hardware Trojan Run-time Detection Through Machine Learning. IEEE Transactions on Computers, 72 (7). pp. 1958-1970.

[img] Text
PCB_Hardware_Trojan_Run-Time_Detection_Through_Machine_Learning.pdf - Author Accepted Manuscript

Download (8MB) | Preview

Abstract

The modern semiconductor electronic devices are becoming increasingly vulnerable to malicious implants called Hardware Trojans (HT). This problem is also greatly related to Printed Circuit Boards (PCB), which are widely used in almost all electronic devices. In this paper, two machine learning (ML) methods have been applied to detect HTs running on power from I/Os of legitimate chips on a PCB. A PCB prototype has been fabricated to obtain real-life data, which was used to train two ML algorithms: One-Class Support Vector Machine and Local Outlier Factor. For validation of the ML classifiers, one hundred categories of HT devices have been modelled and inserted into the Validation and Testing datasets. Simulation results show that using the proposed methodology an HT device can be detected with high prediction accuracy (F1-score above 99.7% for a 50mW HT). Further, the ML model has been uploaded to the prototype PCB for hard-silicon validation of the methodology. To the best of our knowledge, this is the first work on real-time detection of PCB HTs, which are powered from the I/O pins of legitimate ICs. Experimental results show that the performance of the ML model on a real-life prototype is consistent with that of the simulations.

Item Type: Article
Additional Information: (c) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords: Hardware trojan (HT), printed circuit board (PCB), machine learning on microcontroller, one class classification, one-class support vector machine, local outlier factor
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 21 Dec 2022 09:14
Last Modified: 17 Mar 2024 15:50
DOI: 10.1109/TC.2022.3230877
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3166747