Researching | Leakage signal classification and recognition method based on fusion features

Journals >High Power Laser and Particle Beams >Volume 36 >Issue 4 >Page 043018 > Article

High Power Laser and Particle Beams
Vol. 36, Issue 4, 043018 (2024)

Leakage signal classification and recognition method based on fusion features

Yunfeng Kou¹, Fei Dai², Zhiguo Zhao³, Jianming Lü¹, and Xie Ma¹

Author Affiliations

¹Chengdu Xinxinshenfeng Electronics Co, Ltd, Chengdu 611731, China

²Beihang University, Beijing 100083, China

³China Electronics Technology Cyber Security Co., Ltd, Chengdu 610041, China

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DOI: 10.11884/HPLPB202436.230186 Cite this Article

Yunfeng Kou, Fei Dai, Zhiguo Zhao, Jianming Lü, Xie Ma. Leakage signal classification and recognition method based on fusion features[J]. High Power Laser and Particle Beams, 2024, 36(4): 043018 Copy Citation Text

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References

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[2] Liu Wenbin, Wang Menghan, Kou Yunfeng, . Behavior recognition and security application of electronic equipment based on electromagnetic leakage signal[J]. Communications Technology, 52, 1761-1765(2019).

[3] Liu Wenbin, Ding Jianfeng, Kou Yunfeng, . Software-defined electromagnetic leakage technology and its application[J]. Communications Technology, 50, 2094-2099(2017).

[4] Wang Menghan, Kou Yunfeng, Liu Wenbin, . Real-time monitoring and intelligent recognition of electromagnetic leakage signals in computer networks[J]. Communications Technology, 52, 1755-1760(2019).

[5] Guan Tianmin, Han Zhenzhong, Mao Jian. Research on the detection method of electromagnetic information leakage from display by machine learning[J]. Journal of Cyber Security, 6, 101-109(2021).

[6] Xu Yanyun, Zhang Meng, Huang Weiqing. Study on detectable distance for electromagnetic information leakage of information equipment[J]. Journal of Cyber Security, 5, 44-56(2020).

[7] Sehatbakhsh N, Nazari A, Alam M, et al. REMOTE: robust external malware detection framework by using electromagnetic signals[J]. IEEE Transactions on Computers, 69, 312-326(2020).

[8] Werner F T, Yilmaz B B, Prvulovic M, et al. Leveraging EM side-channels for recognizing components on a motherboard[J]. IEEE Transactions on Electromagnetic Compatibility, 63, 502-515(2021).

[9] Jorgensen E J, Werner F T, Prvulovic M, et al. Deep learning classification of motherboard components by leveraging EM side-channel signals[J]. Journal of Hardware and Systems Security, 5, 114-126(2021).

[10] Ding Jianfeng, Liu Wenbin, Ding Lei, . New threat analysis of electromagnetic information leakage in electronic equipment based on active detection[J]. Communications Technology, 51, 936-940(2018).

[11] Ding Jianfeng, Liu Wenbin, Wang Menghan, . Threat analysis of computer information leakage in intermodulation of acoustic, optical and electromagnetic signals[J]. Communications Technology, 52, 967-970(2019).

[12] Cheng Lei, Luo Rujun, Kou Yunfeng, . Verification of conductive electromagnetic information leakage model based on power line[J]. Communications Technology, 51, 941-946(2018).

[13] Qi Guolei, Kou Yunfeng, Hu Hao, . Information leakage based on acoustic convert channel for air-gapped computers[J]. Communications Technology, 51, 700-704(2018).

[14] Hu Hao, Luo Rujun, Qi Guolei, . Covert-optical transmission channel based on LED display[J]. Communications Technology, 51, 1689-1693(2018).

[15] Guri M, Zadov B, Bykhovsky D, et al. PowerHammer: Exfiltrating data from air-gapped computers through power lines[J]. IEEE Transactions on Information Forensics and Security, 15, 1879-1890(2020).

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Yunfeng Kou, Fei Dai, Zhiguo Zhao, Jianming Lü, Xie Ma. Leakage signal classification and recognition method based on fusion features[J]. High Power Laser and Particle Beams, 2024, 36(4): 043018

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