本文關鍵詞： 硬件木馬檢測 功耗 預處理 主成分分析 距離判斷　出處：《天津大學》2016年碩士論文　論文類型：學位論文

【摘要】：硬件木馬是在集成電路設計或制造中對電路的惡意篡改,一旦激活工作,將竊取關鍵信息或者使芯片失效。針對硬件木馬的檢測技術得到廣泛研究,其中基于側信道信息分析的硬件木馬檢測是目前研究較多的檢測方法。側信道檢測方法主要是通過分析側信道信息中硬件木馬引入的差異實現檢測,而數據處理方法對實現有效的檢測至關重要。本文利用側信道分析技術,基于主成分分析法提出了一套數據處理識別算法并采用功耗分析的方法進行驗證。首先對植入了硬件木馬電路的功耗信息建立模型,并基于該模型設計了載體電路和硬件木馬電路。同時優(yōu)化測試環(huán)境和測試技術,從基于FPGA的硬件木馬檢測平臺中獲取了母本電路功耗和植入了硬件木馬的電路功耗。然后,針對獲取的功耗數據樣本,分析數據特點,采用相應的數據處理方法優(yōu)化數據,分類識別。重點是采用預處理方法解決數據的波形未對齊、異常值、噪聲問題,并基于主成分分析法實現了母本數據和含硬件木馬數據的特征提取和特征選擇,得到的主特征涵蓋了原數據99%以上的信息,再采用距離判斷的方法實現硬件木馬的有效檢測。最后,針對基于主成分分析的硬件木馬檢測方法,優(yōu)化預處理算法,提高檢測精度。同時,在改變母本樣本量、采樣頻率等參數的情況下,驗證本方法的適用性,并基于MATLAB開發(fā)了硬件木馬數據處理系統(tǒng)。結果表明,基于主成分分析結合距離判斷的方法能夠有效檢測出占母本電路面積為0.15%左右的硬件木馬。
[Abstract]:Hardware Trojan is the malicious tampering of the circuit in the design or manufacture of integrated circuits. Once activated, it will steal critical information or invalidate the chip. The detection technology of hardware Trojan has been widely studied. The hardware Trojan detection based on the side channel information analysis is the most widely studied detection method. The side channel detection method is mainly through the analysis of the differences in the side channel information introduced by the hardware Trojan horse to achieve detection. The data processing method is very important to realize the effective detection. This paper uses the side channel analysis technology. Based on principal component analysis, a set of data processing identification algorithm is proposed and validated by power analysis. Firstly, the model of power consumption information of Trojan circuit is built. Based on the model, the carrier circuit and the hardware Trojan circuit are designed. At the same time, the test environment and test technology are optimized. From the hardware Trojan detection platform based on FPGA, the power consumption of the mother circuit and the circuit power of the implanted hardware Trojan are obtained. Then, the characteristics of the data are analyzed according to the obtained power consumption data sample. Using the corresponding data processing method to optimize the data, classification and recognition. The emphasis is to use the pre-processing method to solve the data waveform unaligned, abnormal values, noise problems. Based on the principal component analysis method, the feature extraction and feature selection of mother data and Trojan horse data are realized. The main features cover the information of the original data more than 99%. Finally, aiming at the hardware Trojan detection method based on principal component analysis, the preprocessing algorithm is optimized to improve the detection accuracy. The applicability of this method is verified by changing the sample size and sampling frequency, and the hardware Trojan data processing system is developed based on MATLAB. The method based on principal component analysis (PCA) combined with distance judgment can effectively detect the Trojan horse which occupies about 0.15% of the female circuit area.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN407;TP309

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