System Level Hardware Trojan Detection Using Side-Channel Power Analysis and Machine Learning

Gayatri, R; Gayatri, Yendamury; Mitra, CP; Mekala, Sreenivas; Priyatharishini, M.

doi:10.1109/icces48766.2020.9137882

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…With the booming development of computer vision, vision-based techniques are one of the hot research topics in the last decade. A machine learning algorithm based on power analysis was proposed in [10], which aims to obtain the hardware Trojan infection algorithm and use machine learning to detect hardware Trojan implantation. One HT classification method is called Hardware Trojan Learning Analysis (ATLAS) [11], which identifies HT-infected circuits using a gradient boosting (GB) model on data from the gate-level netlist (GLN) stage.…”

Section: Introductionmentioning

confidence: 99%

Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components

et al. 2022

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With the vigorous development of integrated circuit (IC) manufacturing, the harmfulness of defects and hardware Trojans is also rising. Therefore, chip verification becomes more and more important. At present, the accuracy of most existing chip verification methods depends on high-precision sample data of ICs. Paradoxically, it is more challenging to invent an efficient algorithm for high-precision noiseless data. Thus, we recently proposed a fusion clustering framework based on low-quality chip images named High-Frequency Low-Rank Subspace Clustering (HFLRSC), which can provide the data foundation for the verification task by effectively clustering those noisy and low-resolution partial images of multiple target ICs into the correct categories. The first step of the framework is to extract high-frequency texture components. Subsequently, the extracted texture components will be integrated into subspace learning so that the algorithm can not only learn the low-rank space but also retain high-frequency information with texture characteristics. In comparison with the benchmark and state-of-the-art method, the presented approach can more effectively process simulation low-quality IC images and achieve better performance.

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Section: Introductionmentioning

confidence: 99%

Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components

et al. 2022

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“…Similarly, detection approaches based on emission of light [5], or thermal and power maps [6], also seriously depend on relevant instruments' resolution and the area of Trojan circuit. Power dissipation can be adopted to achieve the Trojan horse detection effectively [7,8], but the test system is very complex since it needs additional test circuit and effective test patterns. HT detection can be fulfilled based on the path delay of ring oscillator networks [9], but it is at the cost of some areas to guarantee a high detection resolution.…”

mentioning

confidence: 99%

A novel hardware Trojan detection method based on sweep‐frequency test

Lei

et al. 2022

Electronics Letters

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This paper proposes a novel hardware Trojan detection method based on sweep‐frequency test since Trojan horse threatens information security seriously. An equivalent parasitic and load circuit model, and analysis method are depicted in detail. Experimental results of the amplitude‐frequency responses between the input and output accord with the simulation results. 11 golden chips and 9 Trojan chips are used for the sweep‐frequency tests, and the golden chips can be distinguished from Trojan chips accurately based on the relationship between the peak and valley resonant frequencies, which indicates that the proposed model and detection method are effective.

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Ensured Configuration Security of FPGAs against CAD Attacks

John¹,

Devi²

2022

Lecture Notes in Electrical Engineering

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System Level Hardware Trojan Detection Using Side-Channel Power Analysis and Machine Learning

Cited by 7 publications

References 12 publications

Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components

Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components

A novel hardware Trojan detection method based on sweep‐frequency test

Ensured Configuration Security of FPGAs against CAD Attacks

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