计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 282-288.doi: 10.11896/jsjkx.191100124

• 信息安全 • 上一篇    下一篇

基于能量分类器的抗SSDF攻击协作频谱感知算法

丁诗铭1,2, 王天荆1, 沈航1,2, 白光伟1   

  1. 1 南京工业大学计算机科学与技术学院 南京211816
    2 南京大学计算机软件新技术国家重点实验室 南京210093
  • 收稿日期:2019-11-18 修回日期:2020-04-25 出版日期:2021-02-15 发布日期:2021-02-04
  • 通讯作者: 沈航(hshen@njtech.edu.cn)
  • 作者简介:dwaxer@163.com
  • 基金资助:
    国家自然科学基金项目(61502230,61501224);江苏省自然科学基金项目(BK20150960);江苏省普通高校自然科学研究项目(15KJB520015);江苏省六大高峰人才基金资助项目(RJFW-020);南京市科技计划项目(201608009);南京大学计算机软件新技术国家重点实验室资助项目(KFKT2017B21)

Energy Classifier Based Cooperative Spectrum Sensing Algorithm for Anti-SSDF Attack

DING Shi-ming1,2, WANG Tian-jing1, SHEN Hang1,2, BAI Guang-wei1   

  1. 1 College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China
    2 State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093,China
  • Received:2019-11-18 Revised:2020-04-25 Online:2021-02-15 Published:2021-02-04
  • About author:DING Shi-ming,born in 1995,postgra-duate.Her main research interests include wireless multimedia sensor network and so on.
    SHEN Hang,born in 1984,Ph.D,asso-ciate professor,postgraduate supervisor,is a member of China Computer Federation.His main research interests include wireless multimedia sensor network,mobile Internet and wireless multimedia communication protocol.
  • Supported by:
    The National Natural Science Foundation of China(61502230,51501224),Natural Science Foundation of Jiangsu province,China(BK20150960),Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(15KJB520015),Six Peak Talents Foundation of Jiangsu Province,China(RJFW-020),Nanjing Science and Technology Plan Project,China(201608009) and State Key Laboratory for Novel Software Technology,Nanjing University(KFKT2017B21).

摘要: 频谱感知是认知无线电通信的重要环节,SSDF(Spectrum Sensing Data Falsification)是协作频谱感知面临的严重安全威胁。SSDF攻击方式逐渐呈现动态化的趋势,传统防御算法因假定攻击强度保持不变而难以识别动态的篡改数据。针对动态化的SSDF攻击,提出一种基于能量分类器的抗SSDF攻击协作频谱感知算法。该算法首先通过分析动态SSDF攻击的特性,结合距离判别法将邻居节点能量分类,通过将分类结果与本地结果进行对比来识别恶意邻居节点;然后本地节点在滑动时间窗内根据历史频谱判决信息和当前频谱判决信息建立信誉模型,并由此更新各邻居节点的信誉值;最后,本地节点实施加权协作频谱感知。仿真结果表明:相比LDCSS(Largest Deviation-based distributed Cooperative Spectrum Sensing)算法和RBCSS(Reputation-based Cooperative Spectrum Sensing)算法,所提算法在动态SSDF攻击的攻击强度接近阈值时频谱检测概率分别提高了15%和16%,显著增加了认知网络的协作频谱感知性能,提升了频谱共享的效率。

关键词: 分布式频谱感知, 距离判别, 能量检测法, 数据篡改, 信誉值

Abstract: Spectrum sensing is an important part of cognitive radio communication and is under the serious threats of spectrum sensing data falsification (SSDF) in terms of security,which trends dynamically in the attacking methods nowadays.It is difficult to identify dynamic tampered data using traditional defense algorithms because the traditional ways always assume that the atta-cking strength remains unchanged.Aiming at the dynamic SSDF attack,an energy classifier enabled cooperative spectrum sensing algorithm for anti-SSDF attack is proposed.This algorithm firstly analyzes the characteristics of dynamic SSDF attacks,combines with the distance discriminant method to classify the neighbor users.Then it identifies the malicious neighbor users by comparing the classification results with the local results.Afterward the local user establishes a reputation model under the sliding time window based on the information of historical results,thereby updates the reputation values of neighbor users and will eventually implement weighted cooperative spectrum sensing.The simulation results show that compared with the largest deviation-based distributed cooperative spectrum sensing (LDCSS) algorithm and the reputation-based cooperative spectrum sensing (RBCSS) algorithm,this algorithm provides an increased spectrum detection probability by 15% and 16% respectively when the attacking strength is close to the threshold,which not only significantly increases the cooperative spectrum sensing performance of the cognitive network,but also improves the efficiency of spectrum sharing.

Key words: Data falsification, Distance discrimination, Distributed spectrum sensing, Energy detection algorithm, Trust value

中图分类号: 

  • TP393
[1] SASABE M,NISHIDA T,KASAHARA S.Collaborative spectrum sensing mechanism based on user incentive in cognitive radio networks[J].Computer Communications,2019,147(8):1-13.
[2] SHRIVASTAVA S,RAJESH A,BORA P K.Defense against primary user emulation attacks from the secondary user throughput perspective[J].AEU-International Journal of Electronics and Communications,2018,84(2):131-143.
[3] SHARMA G,SHARMA R.Performance comparison of hardand soft fusion Techniques for Energy Efficient CSS in Cognitive Radio[C]//2018 International Conference on Advanced Computation and Telecommunication (ICACAT).IEEE,2018:1-4.
[4] SEO D,NAM H.A Parallel Multi-Channel Cooperative Spectrum Sensing in Cognitive Radio Networks[C]//2018 International Symposium on Antennas and Propagation (ISAP).IEEE,2018:1-2.
[5] DU R,ZHOU Y,LIU F,et al.An effective collaborative spectrum sensing method against SSDF attack[C]//2017 29th Chinese Control and Decision Conference (CCDC).IEEE,2017:5698-5702.
[6] DAS D,DAS S.An intelligent resource management scheme for SDF-based cooperative spectrum sensing in the presence of primary user emulation attack[J].Computers & Electrical Engineering,2018,69(7):555-571.
[7] ZHANG L,DING G,WU Q,et al.Byzantine attack and defense in cognitive radio networks:A survey[J].IEEE Communications Surveys & Tutorials,2015,17(3):1342-1363.
[8] PENG T,CHEN Y,XIAO J,et al.Improved soft fusion-based cooperative spectrum sensing defense against SSDF attacks[C]//2016 International Conference on Computer,Information and Telecommunication Systems (CITS).IEEE,2016:1-5.
[9] ESLAMI A,KARAMZADEH S.Performance analysis of double threshold energy detection-based spectrum sensing in low SNRs over Nakagami-m fading channels with noise uncertainty[C]//2016 24th Signal Processing and Communication Application Conference (SIU).IEEE,2016:309-312.
[10] LI L,LI F,ZHU J.A method to defense against cooperative SSDF attacks in Cognitive Radio Networks[C]//2013 IEEE International Conference on Signal Processing,Communication and Computing (ICSPCC 2013).IEEE,2013:1-6.
[11] SUN Z,XU Z,HAMMAD M Z.Defending Against Massive SSDF Attacks from a Novel Perspective of Honest SecondaryUsers[J].IEEE Communications Letters,2019,23(10):1696-1699.
[12] AL-MATHEHAJI Y,BOUSSAKTA S,JOHNSTON M,et al.Defeating SSDF attacks with trusted nodes assistance in cognitive radio networks[J].IEEE Sensors Letters,2017,1(4):1-4.
[13] GHAZNAVI M,JAMSHIDI A.A low complexity cluster based data fusion to defense against SSDF attack in cognitive radio networks[J].Computer Communications,2019,138(4):106-114.
[14] YUE W J,ZHENG B Y,MENG Q M,et al.Robust cooperative spectrum sensing schemes for fading channels in cognitive radio networks[J].Science China Information Sciences,2011,54(2):348-359.
[15] GUL N,QURESHI I M,NAVEED A,et al.Secured Soft Combination Schemes Against Malicious-Users in Cooperative Spectrum Sensing[J].Wireless Personal Communications,2019:1-20.
[16] TANG H,YU F R,HUANG M,et al.Distributed consensus-based security mechanisms in cognitive radio mobile ad hoc networks[J].IET communications,2012,6(8):974-983.
[17] HUANG Q D,SUN Q,YAN Q Q.Communication spectrumsensing scheme based on median anti-SSDF attack[J].Journal of Xi'an University of Posts and Telecommunications,2017,22(2):12-17.
[18] ZENG K,PAWELCZAK P,CABRIC D.Reputation-based coope-rative spectrum sensing with trusted nodes assistance[J].IEEE Communications Letters,2010,14(3):226-228.
[19] LI F W,LIU F,ZHU J,et al.Reputation-based secure spectrum situation fusion in distributed cognitive radio networks[J].The Journal of China Universities of Posts and Telecommunications,2015,22(3):110-117.
[20] WANG J,CHEN R,TSAI J J P,et al.Trust-based cooperative spectrum sensing against SSDF attacks in distributed cognitive radio networks[C]//2016 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2016).IEEE,2016:1-6.
[1] 崔平付,任智,曹建玲.
基于博弈的DTMSN路由选择和信任决策
Game-based Routing Selection and Trust Decisions for DTMSN
计算机科学, 2016, 43(Z6): 268-271. https://doi.org/10.11896/j.issn.1002-137X.2016.6A.064
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!