Part I: Evaluation for Hardware Trojan Detection Based on Electromagnetic Radiation

Su, Ting; Li, Shaoqing; Tang, Yuxiang; Chen, Jihua

doi:10.1007/s10836-020-05905-1

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…20,21 , the detection mechanism and noise source of EMR-based HTD methods are discussed. A specialized noise model is proposed and the in uence of each type of noise on this model is analysed.…”

mentioning

confidence: 99%

A Siamese Deep Learning Framework for Efficient Hardware Trojan Detection Using Power Side-Channel Data

Nasr,

mohamed,

shenawy

et al. 2024

Preprint

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Hardware Trojans (HTs) are malicious alterations to the circuitry of integrated circuits (ICs), enabling unauthorized access, data theft, operational disruptions, or even physical harm. Detecting Hardware Trojans (HTD) is paramount for ensuring IC security. This paper introduces a novel Siamese neural network (SNN) framework for non-destructive HTD. The proposed framework can detect HTs by processing power side-channel signals without the need for a golden model of the IC. To obtain the best results, different neural network models such as Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) are integrated individually with SNN. These models are trained on the extracted features from the Trojan Power & EM Side-Channel dataset. The results show that the Siamese LSTM model achieved the highest accuracy of 86.78%, followed by the Siamese GRU model with 83.59% accuracy and the Siamese CNN model with 73.54% accuracy. The comparison shows that of the proposed Siamese LSTM is a promising new approach for HTD and outperform the state-of-the-art methods.

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mentioning

confidence: 99%

A Siamese Deep Learning Framework for Efficient Hardware Trojan Detection Using Power Side-Channel Data

Nasr,

mohamed,

shenawy

et al. 2024

Preprint

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A Siamese deep learning framework for efficient hardware Trojan detection using power side-channel data

Nasr,

Mohamed,

Elshenawy

et al. 2024

Sci Rep

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Hardware Trojans (HTs) are hidden threats embedded in the circuitry of integrated circuits (ICs), enabling unauthorized access, data theft, operational disruptions, or even physical harm. Detecting Hardware Trojans (HTD) is paramount for ensuring IC security. This paper introduces a novel Siamese neural network (SNN) framework for non-destructive HTD. The proposed framework can detect HTs by processing power side-channel signals without the need for a golden model of the IC. To obtain the best results, different neural network models such as Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) are integrated individually with SNN. These models are trained on the extracted features from the Trojan Power & EM Side-Channel dataset. The results show that the Siamese LSTM model achieved the highest accuracy of 86.78%, followed by the Siamese GRU model with 83.59% accuracy and the Siamese CNN model with 73.54% accuracy. The comparison shows that of the proposed Siamese LSTM is a promising new approach for HTD and outperform the state-of-the-art methods.

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A machine learning method for hardware Trojan detection on real chips

et al. 2021

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Due to the global supply chain of integrated circuits (IC) from design to application, Hardware Trojan (HT) may be stealthily inserted into ICs. The effect of HT detection methods are related to the signal-to-noise ratio (SNR) and the Trojan-to-circuit ratio (TCR). Various HT detection methods are designed to target at simulated circuits; however, the effect on real chips is not involved. In the light of detection of HT on real chips with low SNR and low TCR, a machine learning method is proposed and experimented in this paper. It is difficult to directly distinguish the insignificant effect of HT on a modern complex chip. The proposed method extracts statistic features to explore the much rich expression of HT signals and adjusts the distance of different features to separate Trojan circuits from the security ones far apart. The proposed methods are tested on real chips with 10−5 TCR and demonstrated the effect compared to other state-of-the-art methods.

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Part I: Evaluation for Hardware Trojan Detection Based on Electromagnetic Radiation

Cited by 4 publications

References 29 publications

A Siamese Deep Learning Framework for Efficient Hardware Trojan Detection Using Power Side-Channel Data

A Siamese Deep Learning Framework for Efficient Hardware Trojan Detection Using Power Side-Channel Data

A Siamese deep learning framework for efficient hardware Trojan detection using power side-channel data

A machine learning method for hardware Trojan detection on real chips

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