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Abstract
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.
Highlights
With the development of information technology, electronic devices of various functions are continuously designed, such as computers and printers, which generate electromagnetic radiation during use
Electromagnetic waves leaking from the display were captured by Hidema Tanaka [1]. e second category is active leakage, such as the experiment by Kuhn and Anderson [2], which conducts electromagnetic leakage in the form of actively transmitting specified information. is type of electronic leakage is malicious hardware or software in an electronic device that regularly leaks a specified signal to the outside world by controlling the electronic device. is type of electromagnetic leakage is called an electromagnetic Trojan [3]
Because the electromagnetic signal data is very large and has many features, the overall test results are not satisfactory, but it can be seen that LSTM has higher accuracy than CNN and RNN and CNN has the worst performance; the reason may be, in the detection process, the detection system mainly focuses on the working mode of the electromagnetic Trojan signal. e discrimination between the electromagnetic Trojan signal and the normal signal is mainly in the period, so the deep neural network has a better performance
Summary
With the development of information technology, electronic devices of various functions are continuously designed, such as computers and printers, which generate electromagnetic radiation during use. Different from other common computer Trojan viruses, this type of electromagnetic Trojan does not use the system equipment such as the network to exchange information with the outside world, which causes the electromagnetic Trojan to break through physical isolation and be more difficult to detect. Such electromagnetic Trojans threaten the information security of ordinary users but may even threaten the physically isolated internal network. Security and Communication Networks electromagnetic wave data processing scheme cannot effectively detect the new electromagnetic Trojan attack. Our summary study found that the defense methods of electromagnetic Trojans can be divided into the following categories: (1) cutting off the transmission channel; (2) applying protection on the device; (3) electromagnetic wave record analysis. We combine the fast classification function of deep learning to detect abnormal signals. erefore, the protection of information security can greatly improve the detection efficiency of the electromagnetic Trojan signal, improve the security of the physical isolation network, and promote the development and progress of the electromagnetic Trojan detection
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