Visible to the public Sequential Statistical Analysis-Based Method for Attacks Detection in Cognitive Radio Networks

TitleSequential Statistical Analysis-Based Method for Attacks Detection in Cognitive Radio Networks
Publication TypeConference Paper
Year of Publication2022
AuthorsShakhov, Vladimir
Conference Name2022 27th Asia Pacific Conference on Communications (APCC)
Keywordscognitive radio networks, Cognitive Radio Security, emulation, Force, Information security, Intrusion detection, machine learning, pubcrawl, radio transmitters, Resiliency, Sequential analysis, sequential statistical analysis, statistical analysis
AbstractThis Cognitive radio networks are vulnerable to specific intrusions due to the unique cognitive characteristics of these networks. This DoS attacks are known as the Primary User Emulation Attack and the Spectrum Sensing Data Falsification. If the intruder behavior is not statistically identical to the behavior of the primary users, intrusion detection techniques based on observing the energy of the received signals can be used. Both machine learning-based intrusion detection and sequential statistical analysis can be effectively applied. However, in some cases, statistical sequential analysis has some advantages in dealing with such challenges. This paper discusses aspects of using statistical sequential analysis methods to detect attacks in Cognitive radio networks.
DOI10.1109/APCC55198.2022.9943673
Citation Keyshakhov_sequential_2022