本文選題：側信道分析 + 硬件木馬��； 參考：《計算機應用研究》2017年10期

【摘要】：在側信道分析的基礎上,針對芯片中存在的硬件木馬,提出一種基于樸素貝葉斯分類器的硬件木馬檢測。該方法能夠利用訓練樣本集構建分類器,分類器形成后便可將采集到的待測芯片功耗信息準確分類,從而實現硬件木馬檢測。實驗結果表明,對于占電路資源1.49%和2.39%的兩種木馬,貝葉斯分類器的誤判率僅為2.17%,驗證了該方法的有效性和適用性。此外,在與歐氏距離判別法比較時,基于樸素貝葉斯分類器的方法表現出了更高的判別準確率,同時也具有從混雜芯片中識別出木馬芯片與標準芯片的能力,這又是馬氏距離判別法所不具備的。
[Abstract]:On the basis of side channel analysis, a hardware Trojan horse detection based on naive Bayes classifier is proposed for the hardware Trojan horse in the chip. This method can use the training sample set to construct the classifier. After the classifier is formed, the power consumption information of the chip to be tested can be accurately classified, thus the hardware Trojan can be detected. The experimental results show that the Bayesian classifier's error rate is only 2.17 for the Trojan horse which accounts for 1.49% and 2.39% of circuit resources. The validity and applicability of this method are verified. In addition, compared with Euclidean distance discriminant, the method based on naive Bayesian classifier shows higher accuracy, and it also has the ability to recognize Trojan and standard chips from hybrid chips. This is again the Markovian distance discriminant method does not have.
【作者單位】： 北京電子科技學院電子信息工程系;
【基金】：中央高�；究蒲袠I(yè)務費專項資金資助項目(2014GCYY04)
【分類號】：TN407
，

本文編號：1928342