{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:39:42Z","timestamp":1773776382300,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,14]],"date-time":"2023-05-14T00:00:00Z","timestamp":1684022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laboratory Open Fund of Beijing Smart-chip Microelectronics Technology Co., Ltd"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation results show that our ATTR algorithm can filter out a set of trusted users, eliminate the influence of malicious users, and improve the detection performance of the system.<\/jats:p>","DOI":"10.3390\/s23104751","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T08:33:01Z","timestamp":1684139581000},"page":"4751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8712-8888","authenticated-orcid":false,"given":"Gang","family":"Xie","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1104-349X","authenticated-orcid":false,"given":"Xincheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Jinchun","family":"Gao","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,14]]},"reference":[{"key":"ref_1","unstructured":"Marcus, M., Burtle, J., Franca, B., Lahjouji, A., and McNeil, N. 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