An intelligent classification method for Trojan detection based on side-channel analysis

Wang, Chenxu; Li, Jinghu; Yu, Miao; Wang, Jinxiang

doi:10.1587/elex.10.20130602

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Postsilicon detection mainly divides into three categories: side channel analysis, reverse engineering and functional testing. Among them, side channel analysis [3], which is widely applied, compares IC characteristics such as delay, power consumption, and temperature with a golden chip, i.e., malware-free chips. Detecting these malicious events usually requires a golden reference model [4] that is assumed to be Trojan-free.…”

Section: Introductionmentioning

confidence: 99%

“…Detecting these malicious events usually requires a golden reference model [4] that is assumed to be Trojan-free. The main drawback of the golden reference model is that it may be inconclusive or too complex for exhaustive verification, especially for large designs [3]. Another fundamental limitation of side channel approach is that the effect of a small enough Trojan on both of the logic and sidechannel fingerprints of the circuit, can be masked by process variation and noise.…”

Section: Introductionmentioning

confidence: 99%

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SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier

2021

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Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier

2021

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“…These pre-silicon Trojan detection approaches provide a reliable guarantee for the trustworthiness of the circuits before tape out. In the post-silicon stage, however, the side channel based methods [5,6,7] are more flexible and easy to implement, where I/O ports and side-channel parameters are utilized to find abnormal behaviors introduced by the Trojans. While various HT detection approaches have been explored by many researchers, statistical side-channel analysis has been among the most heavily investigated ones [8].…”

Section: Introductionmentioning

confidence: 99%

Golden chip free Trojan detection leveraging electromagnetic side channel fingerprinting

Liu

Yuan

et al. 2019

IEICE Electron. Express

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Side channel based hardware Trojan detection is one of the most investigated schemes to ensure the trustworthiness of integrated circuits (ICs). However, nearly all of the side-channel methods require a golden chip reference, either a trusted fabricated circuit or layout, which is very difficult to access in reality. In this paper, we propose a golden chip free electromagnetic (EM) side channel simulation and statistical Trojan detection framework. We utilize the design data at early stage of the IC lifecycle to generate EM radiation, and the generated EM traces serve as the golden reference. In order to leverage the EM signatures, a neural network algorithm is utilized for Trojan detection. Experimental results on selected AES benchmarks on FPGA show that the proposed method can effectively detect Trojans with the presence of noise and variations.

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“…Post-silicon detection mainly includes side channel analysis, reverse engineering and functional testing. Among them, side channel analysis [2] is widely applied which compares IC characteristics such as delay time, power consumption, and temperature with a golden chip that does not contain any HTs, unfortunately, a golden chip is usually hard to get in practice. On the other hand, pre-silicon design methods often detect HTs on gated level netlists, which can be categorized into two types: the dynamic and static detection.…”

Section: Introductionmentioning

confidence: 99%

Hardware Trojans classification based on controllability and observability in gate-level netlist

Xie

Sun

Chen

et al. 2017

IEICE Electron. Express

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This paper presents a hardware Trojan classification method that performs a static analysis in gate-level netlist. Based on the controllability and observability characteristics extracted in a circuit, the nets are clustered into two groups with the k-means method. Then inter-cluster distance is measured and taken as the major feature for Trojan identification. By combined with three other features in terms of circuit scale statistic number, a complementary representation of Trojan circuits is constructed. Finally, a support vector machine classifier is trained to distinguish the Trojan circuits from genuine circuits. Experimental results on Trust-HUB benchmarks demonstrate that our method can achieve up to 100% true positive rate.

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An intelligent classification method for Trojan detection based on side-channel analysis

Cited by 10 publications

References 4 publications

SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier

SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier

Golden chip free Trojan detection leveraging electromagnetic side channel fingerprinting

Hardware Trojans classification based on controllability and observability in gate-level netlist

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