甘罕, 张洪欣, 李静, 张帆, 赵新杰, 贺鹏飞. 独立成分分析在电磁攻击中的应用[J]. 电波科学学报, 2016, 31(2): 401-405. doi: 10.13443/j.cjors.2015061003
      引用本文: 甘罕, 张洪欣, 李静, 张帆, 赵新杰, 贺鹏飞. 独立成分分析在电磁攻击中的应用[J]. 电波科学学报, 2016, 31(2): 401-405. doi: 10.13443/j.cjors.2015061003
      GAN Han, ZHANG Hongxin, LI Jing, ZHANG Fan, ZHAO Xinjie, HE Pengfei. Independent component analysis applied in electromagnetic attack[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(2): 401-405. doi: 10.13443/j.cjors.2015061003
      Citation: GAN Han, ZHANG Hongxin, LI Jing, ZHANG Fan, ZHAO Xinjie, HE Pengfei. Independent component analysis applied in electromagnetic attack[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(2): 401-405. doi: 10.13443/j.cjors.2015061003

      独立成分分析在电磁攻击中的应用

      Independent component analysis applied in electromagnetic attack

      • 摘要: 电磁泄漏曲线的对齐与有效点的选取是信息安全的重要研究方向.针对曲线过偏移的问题, 提出了一种新的曲线对齐方法——双峰式相关对齐法.在有效抑制曲线过偏移的同时, 实现了曲线的精确对齐通过独立成分分析(Independent Component Analysis, ICA)方法实现了有效点的选取.通过对电磁泄露曲线求得未知的源信号, 由源信号作为特征点进行分类分析.分别采用ICA、主成分分析(Principal Components Analysis, PCA)、PCA-ICA、ICA-PCA四种方法对数据进行了降维处理.通过支持向量机(Support Vector Machine, SVM)对降维后的数据进行分类对比, 最终得出:在10~100维范围内, PCA-ICA的分类效果最佳, ICA其次, 而ICA-PCA的效果最差; 在100~900维的范围内, PCA与ICA-PCA分类效果随着维度的增加几乎呈直线趋势增加.

         

        Abstract: Alignment and valid points selection of electromagnetic leakage curves are important research direction of information security. For the problem of curve excessive shifted, a new method of curve alignment is proposed, whose name is bimodal correlation alignment method. The method effectively controls the curve excessive shift, and also achieves a precise alignment result. It is proposed to use independent component analysis (ICA) to select valid points. By the method of ICA, unknown source signals are obtained from electromagnetic leakage curves for classification analysis. ICA, principal components analysis(PCA), ICA-PCA, and PCA-ICA are adopted for dimensionality reduction. The classification result is got by support vector machine (SVM). It shows that success rate of ICA and PCA-ICA grows rapidly with the increase of the dimensions. PCA-ICA is the best, ICA is better, whice the worst is ICA-PCA, in the dimensionality 10 to 100.In the dimensionality 100 to 900, the success rate grows mostly like a line with the increase of the dimension.

         

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