Biblio

Filters: Author is Xu, Yanyun  [Clear All Filters]
2022-09-09
Guo, Shaoying, Xu, Yanyun, Huang, Weiqing, Liu, Bo.  2021.  Specific Emitter Identification via Variational Mode Decomposition and Histogram of Oriented Gradient. 2021 28th International Conference on Telecommunications (ICT). :1—6.
Specific emitter identification (SEI) is a physical-layer-based approach for enhancing wireless communication network security. A well-done SEI method can be widely applied in identifying the individual wireless communication device. In this paper, we propose a novel specific emitter identification method based on variational mode decomposition and histogram of oriented gradient (VMD-HOG). The signal is decomposed into specific temporal modes via VMD and HOG features are obtained from the time-frequency spectrum of temporal modes. The performance of the proposed method is evaluated both in single hop and relaying scenarios and under three channels with the number of emitters varying. Results depict that our proposed method provides great identification performance for both simulated signals and realistic data of Zigbee devices and outperforms the two existing methods in identification accuracy and computational complexity.
2021-12-20
Huang, Weiqing, Feng, Zhaowen, Xu, Yanyun, Zhang, Ning.  2021.  A Novel Method for Malicious Implanted Computer Video Cable Detection via Electromagnetic Features. 2021 IEEE Wireless Communications and Networking Conference (WCNC). :1–6.
Electromagnetic (EM) radiation is an inherent phenomenon in the operation of electronic information equipment. The side-channel attack, malicious hardware and software implantation attack by using the EM radiation are implemented to steal information. This form of attacks can be used in air-gap information equipment, which bring great danger for information security. The malicious implantation hidden in circuits are difficult to detect. How to detect the implantation is a challenging problem. In this paper, a malicious hardware implantation is analyzed. A method that leverages EM signals for Trojan-embedded computer video cable detection is proposed. The method neither needs activating the Trojan nor requires near-field probe approaching at close. It utilizes recognizable patterns in the spectrum of EM to predict potential risks. This paper focuses on the extraction of feature vectors via the empirical mode decomposition (EMD) algorithm. Intrinsic mode functions (IMFs) are analyzed and selected to be eigenvectors. Using a common classification technique, we can achieve both effective and reliable detection results.