分布式协作频谱感知网络中恶意节点检测和定位方法研究
Detection and Localization of Malicious Nodes in Distributed Cooperative Spectrum Sensing Network
- 电子学报 2022年50卷第6期 页码：1370-1380
- 作者机构：
1.深圳大学电子与信息工程学院，广东深圳 518060
2.鹏城实验室，广东深圳 518055
3.黄淮学院信息工程学院，河南驻马店 463000
- 作者简介：
[ "吴晓晓 女，1982年出生，湖北鄂州人.深圳大学助理教授.主要研究方向为社交网络中的数据挖掘算法、5G通信网络关键技术研究、信道编码理论等." ]
[ "李刚强 男，1989年出生，河南驻马店人.黄淮学院讲师.主要研究方向为社交网络中的数据挖掘算法、分布式协议、机器学习等." ]
[ "张胜利（通讯作者） 男，1978年出生，河北人.深圳大学教授，博士生导师.主要研究方向为无线通信、区块链关键技术、物理层网络编码等. E-mail: zsl@szu.edu.cn" ]
- 基金信息：国家自然科学基金(62171291);广东省自然科学基金面上项目(2021A1515011915);河南省科技攻关项目(212102210142)
DOI：10.12263/DZXB.20210841
中图分类号： TN92;收稿：2021-07-05，
修回：2022-01-31，
纸质出版：2022-06-25
- 稿件说明：
移动端阅览
吴晓晓,李刚强,张胜利.分布式协作频谱感知网络中恶意节点检测和定位方法研究[J].电子学报,2022,50(06):1370-1380.
WU Xiao-xiao,LI Gang-qiang,ZHANG Sheng-li.Detection and Localization of Malicious Nodes in Distributed Cooperative Spectrum Sensing Network[J].ACTA ELECTRONICA SINICA,2022,50(06):1370-1380.
吴晓晓,李刚强,张胜利.分布式协作频谱感知网络中恶意节点检测和定位方法研究[J].电子学报,2022,50(06):1370-1380. DOI： 10.12263/DZXB.20210841.
WU Xiao-xiao,LI Gang-qiang,ZHANG Sheng-li.Detection and Localization of Malicious Nodes in Distributed Cooperative Spectrum Sensing Network[J].ACTA ELECTRONICA SINICA,2022,50(06):1370-1380. DOI： 10.12263/DZXB.20210841.
摘要
认知无线电是解决无线通信能量有效性问题的关键技术，其中频谱感知对于提高频谱的利用效率有着重要意义.针对基于共识的分布式协作频谱感知算法易受到恶意节点数据注入攻击，影响认知网络性能的问题，本文提出了两种基于神经网络的恶意节点检测和定位方法抵制网络内的恶意攻击行为，并采用基于Gossip Learning的联合学习策略进一步增强训练邻域检测和定位模型的鲁棒性.本文在9个认知节点的曼哈顿网络上模拟了分布式频谱感知的过程，并验证所提出方法的有效性.结果表明，所提方法具有良好的恶意节点检测和定位性能，联合学习策略能够使神经网络在样本局部有限的情况下学习到更多的攻击特征，提高本地检测和定位模型的可靠性.
Abstract
Cognitive radio is a key technology to solve the problem of energy efficiency in wireless communication
and spectrum sensing is of great significance for improving the efficiency of spectrum utilization. To solve the problem that the consensus-based distributed cooperative spectrum sensing algorithm is vulnerable to malicious node data injection attacks
we propose two approaches for detecting and localizing malicious nodes based on neural networks. And a collaborative peer-to-peer machine learning protocol(Gossip Learning) is adopted to facilitate training these neural network models. We simulate the process of distributed cooperative spectrum sensing on a 9-node Manhattan network
and verify the effectiveness of the proposed approaches. Numerical results illustrate that the proposed neural network-based approaches can effectively improve the performance of detecting and localizing malicious nodes. The collaborative learning strategy can enable nodes to learn more attack characteristics
and thus make the network more robust to attacks.
关键词
Keywords
references
刘鑫 , 仲伟志 , 井庆丰 . 认知无线电多时隙联合频谱感知方法及优化 [J]. 电子学报 , 2015 , 43 ( 5 ): 895 - 900 .
LIU X , ZHONG W Z , JING Q F . Multi-slot cooperative spectrum sensing method with optimization in cognitive radio [J]. Acta Electronica Sinica , 2015 , 43 ( 5 ): 895 - 900 . (in Chinese)
丁家昕 , 方箭 , 王坦 . 实施动态频谱管理 提高资源利用效率 [J]. 中国无线电 , 2018 , ( 1 ): 25 - 28 .
MITOLA J , MAGUIRE G Q . Cognitive radio: Making software radios more personal [J]. IEEE Personal Communications , 1999 , 6 ( 4 ): 13 - 18 .
季薇 , 李炳星 , 郑宝玉 . 基于信誉与共识的分布式智能入侵防御方案 [J]. 系统工程与电子技术 , 2018 , 40 ( 3 ): 665 - 670 .
JI W , LI B X , ZHENG B Y . Distributed intelligent intrusion prevention scheme based on reputation and consensus [J]. Systems Engineering and Electronics , 2018 , 40 ( 3 ): 665 - 670 . (in Chinese)
YU G H , WU J , LONG C N . Contextual binary gossip: A fast cooperative spectrum sensing algorithm for cognitive radio networks [C]// Proceeding of the 11th World Congress on Intelligent Control and Automation . Piscataway : IEEE , 2014 : 3013 - 3018 .
DEGROOT M H . Reaching a consensus [J]. Journal of the American Statistical Association , 1974 , 69 ( 345 ): 118 - 121 .
SUNDARAM S , GHARESIFARD B . Distributed optimization under adversarial nodes [J]. IEEE Transactions on Automatic Control , 2019 , 64 ( 3 ): 1063 - 1076 .
GENTZ R , WAI H T , SCAGLIONE A , et al . Detection of data injection attacks in decentralized learning [C]// 2015 49th Asilomar Conference on Signals, Systems and Computers . Piscataway : IEEE , 2015 : 350 - 354 .
GENTZ R , WU S X , WAI H T , et al . Data injection attacks in randomized gossiping [J]. IEEE Transactions on Signal and Information Processing Over Networks , 2016 , 2 ( 4 ): 523 - 538 .
WU S X , WAI H T , SCAGLIONE A , et al . Data injection attack on decentralized optimization [C]// 2018 IEEE International Conference on Acoustics, Speech and Signal Processing . Piscataway : IEEE , 2018 : 3644 - 3648 .
黄豪杰 , 吴晓晓 , 李刚强 . 基于区块链智能合约的物联网恶意节点检测和定位 [J]. 物联网学报 , 2020 , 4 ( 2 ): 58 - 69 .
HUANG H J , WU X X , LI G Q . Anomaly detection and location of malicious node for IoT based on smart contract in blockchain network [J]. Chinese Journal on Internet of Things , 2020 , 4 ( 2 ): 58 - 69 . (in Chinese)
LI Z Q , YU F R , HUANG M Y . A distributed consensus-based cooperative spectrum-sensing scheme in cognitive radios [J]. IEEE Transactions on Vehicular Technology , 2010 , 59 ( 1 ): 383 - 393 .
CHAMLEY C , SCAGLIONE A , LI L . Models for the diffusion of beliefs in social networks: An overview [J]. IEEE Signal Processing Magazine , 2013 , 30 ( 3 ): 16 - 29 .
BOYD S , GHOSH A , PRABHAKAR B , et al . Randomized gossip algorithms [J]. IEEE Transactions on Information Theory , 2006 , 52 ( 6 ): 2508 - 2530 .
DIMAKIS A G , KAR S , MOURA J M F , et al . Gossip algorithms for distributed signal processing [J]. Proceedings of the IEEE , 2010 , 98 ( 11 ): 1847 - 1864 .
PATEL S , KHATANA V , SARASWAT G , et al . Distributed detection of malicious attacks on consensus algorithms with applications in power networks [C]// 2020 7th International Conference on Control, Decision and Information Technologies(CoDIT) . Piscataway : IEEE , 2020 : 397 - 402 .
SCHÖLKOPF B , PLATT J , HOFMANN T . Multi-instance multi-label learning with application to scene classification [C]// Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference . Commonwealth : MIT Press , 2006 : 1609 - 1616 .
SVOZIL D , KVASNICKA V , POSPICHAL J . Introduction to multi-layer feed-forward neural networks [J]. Chemometrics and Intelligent Laboratory Systems , 1997 , 39 ( 1 ): 43 - 62 .
WANG H Z , LI G Q , WANG G B , et al . Deep learning based ensemble approach for probabilistic wind power forecasting [J]. Applied Energy , 2017 , 188 : 56 - 70 .
GIARETTA L , GIRDZIJAUSKAS Š . Gossip learning: off the beaten path [C]// 2019 IEEE International Conference on Big Data(Big Data) . Piscataway : IEEE , 2019 : 1117 - 1124 .
JIANG Z H , BALU A , HEGDE C , et al . Collaborative deep learning in fixed topology networks [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach : MIT Press , 2017 : 5906 - 5916 .
BLOT M , PICARD D , THOME N , et al . Distributed optimization for deep learning with gossip exchange [J]. Neurocomputing , 2019 , 330 : 287 - 296 .
FAWCETT T . An introduction to ROC analysis [J]. Pattern Recognition Letters , 2006 , 27 ( 8 ): 861 - 874 .
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