MERS: Statistical Test Generation for Side-Channel Analysis based Trojan Detection
Yuanwen Huang,Swarup Bhunia,Prabhat Mishra +2 more
- pp 130-141
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TLDR
Simulation results demonstrate that the tests generated by MERS can significantly increase the Trojans sensitivity, thereby making Trojan detection effective using side-channel analysis.Abstract:
Hardware Trojan detection has emerged as a critical challenge to ensure security and trustworthiness of integrated circuits. A vast majority of research efforts in this area has utilized side-channel analysis for Trojan detection. Functional test generation for logic testing is a promising alternative but it may not be helpful if a Trojan cannot be fully activated or the Trojan effect cannot be propagated to the observable outputs. Side-channel analysis, on the other hand, can achieve significantly higher detection coverage for Trojans of all types/sizes, since it does not require activation/propagation of an unknown Trojan. However, they have often limited effectiveness due to poor detection sensitivity under large process variations and small Trojan footprint in side-channel signature. In this paper, we address this critical problem through a novel side-channel-aware test generation approach, based on a concept of Multiple Excitation of Rare Switching (MERS), that can significantly increase Trojan detection sensitivity. The paper makes several important contributions: i) it presents in detail the statistical test generation method, which can generate high-quality testset for creating high relative activity in arbitrary Trojan instances; ii) it analyzes the effectiveness of generated testset in terms of Trojan coverage; and iii) it describes two judicious reordering methods can further tune the testset and greatly improve the side channel sensitivity. Simulation results demonstrate that the tests generated by MERS can significantly increase the Trojans sensitivity, thereby making Trojan detection effective using side-channel analysis.read more
Citations
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Journal ArticleDOI
COTD: Reference-Free Hardware Trojan Detection and Recovery Based on Controllability and Observability in Gate-Level Netlist
TL;DR: Using an unsupervised clustering analysis, the paper shows that the controllability and observability characteristics of Trojan gates present significant inter-cluster distance from those of genuine gates in a Trojan-inserted circuit, such that Trojan gates are easily distinguishable.
Journal ArticleDOI
Hardware Trojan Detection Through Chip-Free Electromagnetic Side-Channel Statistical Analysis
TL;DR: Experimental results on selected Advanced Encryption Standard benchmark circuits on FPGA show that the proposed method can effectively detect Trojans even with very small traces, and is immune to the process variation theoretically.
Journal ArticleDOI
Scalable Test Generation for Trojan Detection Using Side Channel Analysis
TL;DR: Simulation results demonstrate that the tests generated by MERS can significantly increase the Trojans sensitivity, thereby making Trojan detection effective using side-channel analysis.
Proceedings ArticleDOI
An automated configurable Trojan insertion framework for dynamic trust benchmarks
TL;DR: Experiments demonstrate that a state-of-the-art Trojan detection technique provides poor efficacy when using benchmarks generated by the developed comprehensive framework of automatic hardware Trojan insertion.
Proceedings ArticleDOI
Hardware Trojan Detection Using ATPG and Model Checking
TL;DR: This paper proposes an effective test generation approach which is capable of activating malicious functionality hidden in large sequential designs and uses the combination of ATPG and model checking approaches to detect hardware Trojans.
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MERO: A Statistical Approach for Hardware Trojan Detection
TL;DR: A test pattern generation technique based on multiple excitation of rare logic conditions at internal nodes that maximizes the probability of inserted Trojans getting triggered and detected by logic testing, while drastically reducing the number of vectors compared to a weighted random pattern based test generation.