基于长短时记忆神经网络的硬件木马检测

doi:10.19678/j.issn.1000-3428.0055589

摘要/Abstract

摘要： 硬件木马给集成电路芯片的可靠性带来巨大威胁，为此，提出一种基于主成分分析（PCA）和长短时记忆（LSTM）神经网络的硬件木马检测方法。利用PCA提取侧信道信息中的电流特征向量，并利用该特征向量训练LSTM神经网络分类器，使该分类器达到识别硬件木马的目的。实验结果表明，该方法能对木马进行有效识别，且能检测出木马面积占总电路面积比为0.74%的硬件木马。

关键词: 硬件木马检测, 集成电路, 旁路信息, 主成分分析, 长短时记忆神经网络

Abstract: Hardware Trojans pose a huge threat to the reliability of integrated circuit chips.Therefore,this paper proposes a hardware Trojan detection method based on Principal Component Analysis(PCA) and Long Short-Term Memory(LSTM) neural network.The method uses PCA to extract the feature vector of current in bypass information,and the extracted feature vector is used to train the LSTM network classifier for hardware Trojan recognition.Experimental results show that the proposed method can effectively identify Trojans,and can detect hardware Trojans that occupy only 0.74% of the total circuit area.

Key words: hardware Trojan detection, integrated circuit, bypass information, Principal Component Analysis(PCA), Long Short-Term Memory(LSTM) neural network

中图分类号:

TN918

胡涛, 佃松宜, 蒋荣华. 基于长短时记忆神经网络的硬件木马检测[J]. 计算机工程, 2020, 46(7): 110-115.

HU Tao, DIAN Songyi, JIANG Ronghua. Hardware Trojan Detection Based on Long Short-Term Memory Neural Network[J]. Computer Engineering, 2020, 46(7): 110-115.

https://www.ecice06.com/CN/Y2020/V46/I7/110

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