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Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing.
Gang Xie1, Xincheng Zhou2, Jinchun Gao3
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|July 11, 2023
Summary
This study introduces an adaptive trust threshold model using reinforcement learning to combat spectrum-sensing data falsification attacks in cooperative spectrum sensing systems. The algorithm effectively identifies and filters malicious users, enhancing overall system detection performance.
Area of Science:
- Electrical Engineering
- Computer Science
- Cybersecurity
Background:
- Cooperative spectrum sensing (CSS) enhances cognitive radio system performance.
- Malicious users (MUs) exploit CSS to launch spectrum-sensing data falsification (SSDF) attacks.
- Existing methods struggle to differentiate between honest and malicious users effectively.
Purpose of the Study:
- To propose an adaptive trust threshold model (ATTR) for detecting SSDF attacks.
- To enhance the resilience of cooperative spectrum sensing against malicious activities.
- To improve the overall detection performance in the presence of both ordinary and intelligent SSDF attacks.
Main Methods:
- Developed an adaptive trust threshold model (ATTR) based on reinforcement learning.
- The ATTR algorithm learns attack strategies of malicious users.
- Implemented dynamic trust threshold adjustments for collaborating users.
Main Results:
- The ATTR algorithm successfully filters out trusted users.
- The influence of malicious users on the sensing performance is eliminated.
- Significant improvement in the system's detection performance was observed.
Conclusions:
- The proposed ATTR algorithm is effective in mitigating SSDF attacks in CSS.
- Reinforcement learning provides an adaptive approach to managing trust in dynamic environments.
- ATTR enhances the reliability and security of cognitive radio systems.
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