Hardware Trojan Detection Using Deep Learning-Generative Adversarial Network and Stacked Auto Encoder Neural Networks

Jose, Fredin; Priyatharishini, M.; Devi, M. Nirmala

doi:10.1007/978-981-16-5655-2_19

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…It uses Graph Neural Networks (GNNs) to learn the circuit behavior through a Data Flow Graph (DFG) representation of hardware design. Fredin et al33 , suggest using gate-level netlists to detect HTs. The netlists are used to create datasets with extracted features for DL models.…”

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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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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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Hardware Trojan Detection Using Machine Learning: A Tutorial

Gubbi

Latibari

Srikanth

et al. 2023

ACM Trans. Embed. Comput. Syst.

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With the growth and globalization of IC design and development, there is an increase in the number of Designers and Design houses. As setting up a fabrication facility may easily cost upwards of $20 billion, costs for advanced nodes may be even greater. IC design houses that cannot produce their chips in-house have no option but to use external foundries that are often in other countries. Establishing trust with these external foundries can be a challenge, and these foundries are assumed to be untrusted. The use of these untrusted foundries in the global semiconductor supply chain has raised concerns about the security of the fabricated ICs targeted for sensitive applications. One of these security threats is the adversarial infestation of fabricated ICs with a Hardware Trojan (HT). An HT can be broadly described as a malicious modification to a circuit to control, modify, disable, or monitor its logic. Conventional VLSI manufacturing tests and verification methods fail to detect HT due to the different and un-modeled nature of these malicious modifications. Current state-of-the-art HT detection methods utilize statistical analysis of various side-channel information collected from ICs, such as power analysis, power supply transient analysis, regional supply current analysis, temperature analysis, wireless transmission power analysis, and delay analysis. To detect HTs, most methods require a Trojan-free reference golden IC. A signature from these golden ICs is extracted and used to detect ICs with HTs. However, access to a golden IC is not always feasible. Thus, a mechanism for HT detection is sought that does not require the golden IC. Machine Learning (ML) approaches have emerged to be extremely useful in helping eliminate the need for a golden IC. Recent works on utilizing ML for HT detection have been shown to be promising in achieving this goal. Thus, in this tutorial, we will explain utilizing ML as a solution to the challenge of HT detection. Additionally, we will describe the Electronic Design Automation (EDA) tool flow for automating ML-assisted HT detection. Moreover, to further discuss the benefits of ML-assisted HT detection solutions, we will demonstrate a Neural Network (NN)-assisted timing profiling method for HT detection. Finally, we will discuss the shortcomings and open challenges of ML-assisted HT detection methods.

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Hardware Trojan Detection Using Deep Learning-Generative Adversarial Network and Stacked Auto Encoder Neural Networks

Cited by 3 publications

References 12 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

Hardware Trojan Detection Using Machine Learning: A Tutorial

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