A SSDF Attack Detection Algorithm Based on Integration Learning and Deep Learning
DOI:
https://doi.org/10.62051/ijcsit.v5n3.07Keywords:
SSDF, Spectrum sensing, Ensemble learning, Deep learning, AccuracyAbstract
Spectrum sensing data falsification (SSDF) attacks are one of the most serious security threats in collaborative spectrum sensing models. In order to deal with SSDF attacks, an attack detection algorithm combining ensemble learning and deep learning is proposed. The algorithm uses soft voting mechanism to integrate the advantages of traditional integrated learning methods of support vector machine (SVM), decision tree and random forest, fully mining the features of the data, combined with the powerful automatic feature extraction capability of deep learning, to achieve accurate detection of SSDF attacks. At the same time, the multi-mode hybrid attack training and detection system can enhance the detection accuracy through continuous optimization, and improve the flexibility and reliability of the system in practical application. Experiments show that the proposed algorithm can effectively identify SSDF attack behavior, perform stably under different attack scenarios, and have strong robustness, which can effectively guarantee the security of spectrum sensing system.
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