Scholarlink

<p>Video Display Units (VDUs) serve as ubiquitous interfaces between humans and machines; however, their unintended electromagnetic emissions during signal transmission pose significant risks of information leakage. Traditional methods for leakage assessment are inherently subjective, as they rely on expert interpretation or screen content reconstruction. This study systematically examines the temporal and statistical characteristics of electromagnetic leakage signals from VGA cables, employing differential information entropy to quantify the features of these information-bearing emissions. We further propose an attention-based CNN-LSTM model enhanced with information entropy, designed to identify leakage-sensitive electromagnetic signals (“red signals”), without requiring reconstruction of the original screen content. Experiments conducted with real VDUs demonstrate the model’s high accuracy and robustness. The proposed framework serves as an effective tool for characterizing electromagnetic signals and assessing leakage risks associated with display devices, offering a data-driven approach to evaluating the electromagnetic security of information technology equipment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An information entropy enhanced CNN-LSTM analysis framework for electromagnetic leakage detection of display device

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月14日

查看全文
  • Yiqi Yan,
  • Xingye Chen,
  • Fei Dai

摘要

Video Display Units (VDUs) serve as ubiquitous interfaces between humans and machines; however, their unintended electromagnetic emissions during signal transmission pose significant risks of information leakage. Traditional methods for leakage assessment are inherently subjective, as they rely on expert interpretation or screen content reconstruction. This study systematically examines the temporal and statistical characteristics of electromagnetic leakage signals from VGA cables, employing differential information entropy to quantify the features of these information-bearing emissions. We further propose an attention-based CNN-LSTM model enhanced with information entropy, designed to identify leakage-sensitive electromagnetic signals (“red signals”), without requiring reconstruction of the original screen content. Experiments conducted with real VDUs demonstrate the model’s high accuracy and robustness. The proposed framework serves as an effective tool for characterizing electromagnetic signals and assessing leakage risks associated with display devices, offering a data-driven approach to evaluating the electromagnetic security of information technology equipment.