The electromagnetic Trojan attack can break through the physical isolation to attack, and the leaked channel does not use the system network resources, which makes the traditional firewall and other intrusion detection devices unable to effectively prevent. Based on the existing research results, this paper proposes an electromagnetic Trojan detection method based on deep learning, which makes the work of electromagnetic Trojan analysis more intelligent. First, the electromagnetic wave signal is captured using software-defined radio technology, and then the signal is initially filtered in combination with a white list, a demodulated signal, and a rate of change in intensity. Secondly, the signal in the frequency domain is divided into blocks in a time-window mode, and the electromagnetic signals are represented by features such as time, information amount, and energy. Finally, the serialized signal feature vector is further extracted using the LSTM algorithm to identify the electromagnetic Trojan. This experiment uses the electromagnetic Trojan data published by Gurion University to test. And it can effectively defend electromagnetic Trojans, improve the participation of computers in electromagnetic Trojan detection, and reduce the cost of manual testing.

中文翻译：

---

基于深度学习的电磁木马检测研究与分析

特洛伊木马的电磁攻击可以突破物理隔离进行攻击，泄漏的通道不占用系统网络资源，这使得传统的防火墙等入侵检测设备无法有效防范。在现有研究成果的基础上，提出了一种基于深度学习的电磁木马检测方法，使电磁木马分析工作更加智能。首先，使用软件定义的无线电技术捕获电磁波信号，然后首先结合白名单，解调信号和强度变化率对信号进行滤波。其次，在时窗模式下将频域中的信号分为多个块，并且电磁信号由诸如时间，信息量和能量。最后，使用LSTM算法进一步提取序列化的信号特征向量，以识别电磁木马。本实验使用Gurion大学发布的电磁Trojan数据进行测试。它可以有效地防御电磁木马，提高计算机在电磁木马检测中的参与度，并降低人工测试的成本。