基于木馬特征風險敏感的硬件木馬檢測方法
電子技術應用
李林源,徐金甫,嚴迎建,劉燕江
(信息工程大學 信息安全重點實驗室,河南 鄭州 450000)
摘要: 針對現(xiàn)有硬件木馬檢測方法中存在的木馬檢出率偏低問題,提出一種基于木馬特征風險敏感的門級硬件木馬檢測方法。通過分析木馬電路的結構特征和信號特征,構建11維硬件木馬特征向量;提出了基于Borderline-SMOTE的硬件木馬特征擴展算法,有效擴充了訓練數(shù)據(jù)集中的木馬樣本信息;基于PSO智能尋優(yōu)算法優(yōu)化SVM模型參數(shù),建立了木馬特征風險敏感分類模型。該方法基于Trust-Hub木馬庫中的17個基準電路展開實驗驗證,其中16個基準電路的平均真陽率(TPR)達到100%,平均真陰率(TNR)高達99.04%,與現(xiàn)有的其他檢測方法相比,大幅提升了硬件木馬檢出率。
中圖分類號:TP309+.1
文獻標志碼:A
DOI: 10.16157/j.issn.0258-7998.223339
中文引用格式: 李林源,徐金甫,嚴迎建,等. 基于木馬特征風險敏感的硬件木馬檢測方法[J]. 電子技術應用,2023,49(6):35-43.
英文引用格式: Li Linyuan,Xu Jinfu,Yan Yingjian,et al. Hardware Trojan detection method based upon Trojan cost-sensitive[J]. Application of Electronic Technique,2023,49(6):35-43.
文獻標志碼:A
DOI: 10.16157/j.issn.0258-7998.223339
中文引用格式: 李林源,徐金甫,嚴迎建,等. 基于木馬特征風險敏感的硬件木馬檢測方法[J]. 電子技術應用,2023,49(6):35-43.
英文引用格式: Li Linyuan,Xu Jinfu,Yan Yingjian,et al. Hardware Trojan detection method based upon Trojan cost-sensitive[J]. Application of Electronic Technique,2023,49(6):35-43.
Hardware Trojan detection method based upon Trojan cost-sensitive
Li Linyuan,Xu Jinfu,Yan Yingjian,Liu Yanjiang
(Key Laboratory of Information Security, Information Engineering University, Zhengzhou 450000, China)
Abstract: In the existing hardware Trojan detection methods, there is problem of low detection rate. Therefore, a cost-sensitive hardware Trojan detection was proposed. By analyzing the structural and signal features of Trojan circuits, an 11 dimensional Trojan feature vector was established. A Trojan feature expansion algorithm based on Borderline-SMOTE was proposed, which effectively expanded the Trojan sample information in the training set. Based on PSO algorithm, the parameters of SVM model were optimized, and a cost-sensitive classification model was established. 17 benchmark circuits from the Trust-Hub were used to verify the efficacy of the proposed approach. Among them, the TPR of 16 benchmark circuits is 100%, and the average TNR is as high as 99.04%. Compared with other existing methods, the detection rate of Trojan is improved greatly.
Key words : hardware Trojan detection;cost-sensitive;PSO;SVM classification model
0 引言
近些年來,隨著半導體產業(yè)的蓬勃發(fā)展,集成電路(IC)設計和制造的外包已成常態(tài),這為惡意的第三方供應商在IC中植入硬件木馬提供了機會。硬件木馬一旦被激活,可能導致IC功能的改變、泄露內部信息、降低電路可靠性,甚至使芯片失效??紤]到木馬電路為硬件安全帶來的巨大威脅,硬件木馬檢測的研究一直在積極進行。然而,硬件木馬的設計和檢測相互促進、同步發(fā)展,即一種新的檢測方法被提出后,攻擊者會立即設計出一種新的硬件木馬,以規(guī)避該檢測方法。因此,如何實現(xiàn)對未知硬件木馬的有效檢測是一個亟待解決的問題。鑒于此問題,一種基于機器學習的硬件木馬檢測方法被提出,通過分析和提取木馬電路的特征,建立硬件木馬特征數(shù)據(jù)庫,應用機器學習模型進行分類器的訓練,使用訓練好的分類器檢測門級網表中可能被植入的硬件木馬。該方法不需要純凈的黃金網表作為參考,當新類型的硬件木馬出現(xiàn)時,可以通過更新特征數(shù)據(jù)庫擴大檢測范圍,實現(xiàn)對新型木馬的覆蓋,因而得到廣泛的研究。
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作者信息:
李林源,徐金甫,嚴迎建,劉燕江
(信息工程大學 信息安全重點實驗室,河南 鄭州 450000)
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