• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.9
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• pp.844-853
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• 2016

Generally, the compromising electromagnetic emanations are generated from LCD(Liquid Crystal Display) monitor which is typical output component of computer. Because display information transmitted to LCD monitor is included in these emanations, there are risks about information leakage of monitor by eavesdropping of leaked signal. So, analysis about possibility of information leakage is necessary because electromagnetic security through the electromagnetic emanations is being at issue. In this paper, the possibility of display information leakage are demonstrated by analyzing the electromagnetic emanations from desktop and laptop monitors. The characteristics of leaked signal from LCD monitor is verified by analyzing display mechanism and the electromagnetic emanations are measured in the long distance by eavesdropping experiment. Also, threat of information leakage is confirmed by recovering display information with several signal processing technique and comprising with target display.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.11
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• pp.1004-1011
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• 2016

In this paper, we suggested the methodology of analyzing and reconstructing of measured electromagnetic emanations from the Micro Controller Unit(MCU) chip of Universal Serial Bus(USB) keyboard. By analyzing electromagnetic emanations, entered information is found at keystroke and furthermore, information security problems such as personal information leakage and eavesdropping can be arisen. USB keyboards make the radiated signal according to the signal transmission mechanism. Electromagnetic emanations were measured by log periodic antenna and wideband receiver and were analyzed by signal processing algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.1 s.116
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• pp.76-81
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• 2007

In this paper, we analyzed the vulnerability of information leakage due to the leakage electromagnetic waves of a PC keyboard. First, we reviewed the keyboard protocol and hardware structure, we analyzed the correlation between the data signal, which is transmitted from the keyboard to the main body, and the leakage signal on the power cable. With the result, we grasped the cause of the Conducted Emission of a PC keyboard. Also, we compared the limit level of the CISPR 22 standard with the amplitude of the keyboard leakage electromagnetic waves we calculated. By analyzing the signal on the power cable of the PC main body through the simple experiment, we show that it is possible to extract the contents of the PC key. Therefore it is verified that the secret information of the PC user could leak out.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.738-754
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• 2024

Side-channel analysis (SCA) is a cryptanalytic technique that exploits physical leakages, such as power consumption or electromagnetic emanations, from cryptographic devices to extract secret keys used in cryptographic algorithms. Recent studies have shown that training SCA models with semi-supervised learning can effectively overcome the problem of few labeled power traces. However, the process of training SCA models using semi-supervised learning generates many pseudo-labels. The performance of the SCA model can be reduced by some of these pseudo-labels. To solve this issue, we propose the HWFilter method to improve semi-supervised SCA. This method uses a Hamming Weight Pseudo-label Filter (HWPF) to filter the pseudo-labels generated by the semi-supervised SCA model, which enhances the model's performance. Furthermore, we introduce a normal distribution method for constructing the HWPF. In the normal distribution method, the Hamming weights (HWs) of power traces can be obtained from the normal distribution of power points. These HWs are filtered and combined into a HWPF. The HWFilter was tested using the ASCADv1 database and the AES_HD dataset. The experimental results demonstrate that the HWFilter method can significantly enhance the performance of semi-supervised SCA models. In the ASCADv1 database, the model with HWFilter requires only 33 power traces to recover the key. In the AES_HD dataset, the model with HWFilter outperforms the current best semi-supervised SCA model by 12%.