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.

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With the development of multi-element integration and high elastic power grid construction, digital sensing of transmission, transformation and distribution equipment in many new energy sources, virtual power plants and load aggregators will be widely used. In this environment, it is urgent to build a reliable sensing network to support its digital sensing applications such as operation state sensing, data collection and demand management. However, due to the large number and wide distribution of distribution equipment, there may be errors in the uploading results of sensors. This paper proposes an efficient security detection strategy based on K-Means algorithm, which greatly improves the information integration and decision ability of the data center.