电讯技术2026，Vol.66Issue(2)：183-190,8.DOI:10.20079/j.issn.1001-893x.241107004
一种基于信誉值多分类与在线学习的安全频谱感知策略
A Secure Spectrum Sensing Strategy Based on Reputation Value Multi-class Classification and Online Learning
摘要
Abstract
For the problem of spectrum sensing data falsification(SSDF)attack leading to the degradation of cooperative spectrum sensing performance,a secure spectrum sensing strategy based on multi-classification of reputation values and online learning is proposed.Firstly,the average energy difference value and the average amplitude difference value of the uploaded signal of secondary user(SU)are extracted to train the malicious secondary user(MSU)identification model.Then,the reputation value of each SU is calculated through the judgment results of the historical MSU identification model.And then SU is divided into normal secondary user and MSU according to the reputation value,and MSU is further divided into occasional,regular and frequent attacks.Corresponding processing methods are adopted for different types to extract the features of their uploaded signals,so as to train the primary user(PU)state decision model more fully and improve the ability to resist SSDF attacks.Finally,online learning is used to update the reputation value,MSU identification model and PU status decision model of each SU in real time,and the maximum length of the training set is limited to adjust the type of MSU in time,so as to improve the defense ability of the latent attack with stronger concealment.Simulation results demonstrate that the detection probability of the proposed method under latent attack is up to 84.43%,which shows that it has a better ability against SSDF attack than existing methods.关键词
协作频谱感知/频谱感知数据伪造(SSDF)/信誉值/在线学习Key words
cooperative spectrum sensing/spectrum sensing data falsification(SSDF)/reputation value/online learning分类
信息技术与安全科学引用本文复制引用
谢松霖,顾志豪,王全全,胡海峰,吴城坤..一种基于信誉值多分类与在线学习的安全频谱感知策略[J].电讯技术,2026,66(2):183-190,8.基金项目
国家自然科学基金资助项目(62371245) （62371245）
江苏省教育科学规划重点课题(B/2023/01/120) （B/2023/01/120）
