Nasr Abdurrahman, Mohamed Khalil, Elshenawy Ayman, Zaki Mohamed
Faculty of Engineering, Systems and Computers Engineering Department, Al-Azhar University, Nasr City, Cairo, Egypt.
Sci Rep. 2024 Jun 6;14(1):13013. doi: 10.1038/s41598-024-62744-2.
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.
硬件木马（HTs）是嵌入在集成电路（IC）电路中的隐藏威胁，可导致未经授权的访问、数据盗窃、操作中断，甚至造成物理损害。检测硬件木马（HTD）对于确保集成电路安全至关重要。本文介绍了一种用于非破坏性HTD的新型暹罗神经网络（SNN）框架。所提出的框架可以通过处理功率侧信道信号来检测硬件木马，而无需集成电路的黄金模型。为了获得最佳结果，将不同的神经网络模型，如卷积神经网络（CNN）、门控循环单元（GRU）和长短期记忆网络（LSTM）分别与SNN集成。这些模型在从木马功率与电磁侧信道数据集中提取的特征上进行训练。结果表明，暹罗LSTM模型实现了最高精度86.78%，其次是暹罗GRU模型，精度为83.59%，暹罗CNN模型精度为73.54%。比较表明，所提出的暹罗LSTM是一种有前途的HTD新方法，优于现有方法。
