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2020-12-11
Kousri, M. R., Deniau, V., Gransart, C., Villain, J..  2019.  Optimized Time-Frequency Processing Dedicated to the Detection of Jamming Attacks on Wi-Fi Communications. 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC). :1—4.

Attacks by Jamming on wireless communication network can provoke Denial of Services. According to the communication system which is affected, the consequences can be more or less critical. In this paper, we propose to develop an algorithm which could be implemented at the reception stage of a communication terminal in order to detect the presence of jamming signals. The work is performed on Wi-Fi communication signals and demonstrates the necessity to have a specific signal processing at the reception stage to be able to detect the presence of jamming signals.

2018-01-10
Shi, Z., Huang, M., Zhao, C., Huang, L., Du, X., Zhao, Y..  2017.  Detection of LSSUAV using hash fingerprint based SVDD. 2017 IEEE International Conference on Communications (ICC). :1–5.
With the rapid development of science and technology, unmanned aerial vehicles (UAVs) gradually become the worldwide focus of science and technology. Not only the development and application but also the security of UAV is of great significance to modern society. Different from methods using radar, optical or acoustic sensors to detect UAV, this paper proposes a novel distance-based support vector data description (SVDD) algorithm using hash fingerprint as feature. This algorithm does not need large number of training samples and its computation complexity is low. Hash fingerprint is generated by extracting features of signal preamble waveforms. Distance-based SVDD algorithm is employed to efficiently detect and recognize low, slow, small unmanned aerial vehicles (LSSUAVs) using 2.4GHz frequency band.