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Recognition Of Electro-magnetic Leakage Information From Computer Radiation With Svm

机译:利用Svm识别计算机辐射中的电磁泄漏信息

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摘要

This paper focuses on the far-field reception of electromagnetic (EM) radiation and the recognition of letters recovered from the EM leakage. EM radiation captured by a wideband antenna is strengthened by a pre-manipulation system with amplifiers and filters, and the useful information is extracted. After being recovered from the EM radiation with signal processing method, the text image is further recognized by support vector machine (SVM) algorithm. Here, a two-layered SVM network with 60 SVMs is constructed to recognize the letters. In the process, the text image is cut apart into single letters to meet the input requirement of letter-based SVMs. To handle those exceptions of stroke connections, an interactive method and an automatic method are proposed. The received text image is usually obtained in low resolution with characteristics of large stroke distortion, font variation and variable size. In our two applications, however, the recognition accuracy reached 99.2% for the larger font size texts and 96.4% for the smaller size texts. From this, we may draw a conclusion that the proposed SVMs network works well in recognizing textual information and emphasize the potential risk of information leakage for computer system.
机译:本文着重介绍电磁(EM)辐射的远场接收以及从EM泄漏中恢复的字母的识别。带有放大器和滤波器的预操纵系统可增强宽带天线捕获的EM辐射,并提取有用的信息。通过信号处理方法从EM辐射中恢复后,文本图像将进一步通过支持向量机(SVM)算法进行识别。在此,构建了一个具有60个SVM的两层SVM网络来识别字母。在此过程中,文本图像被切成单个字母,以满足基于字母的SVM的输入要求。为了处理笔触连接的异常情况,提出了一种交互式方法和一种自动方法。通常以低分辨率获得接收到的文本图像,该图像具有较大的笔画失真,字体变化和大小可变的特征。但是,在我们的两个应用程序中,较大字体的文本的识别精度达到99.2%,而较小字体的文本则达到96.4%。由此,我们可以得出一个结论,即所提出的SVM网络在识别文本信息方面表现良好,并强调了计算机系统信息泄漏的潜在风险。

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