基于Dempster-Shafer证据理论与抗频谱感知数据篡改攻击的协作式频谱检测算法

doi:10.3969/j.issn.1000-1093.2017.12.014

摘要/Abstract

摘要： 针对恶意用户伪造认知用户或者控制已有认知用户、频谱感知数据篡改（SSDF）、使现有协作式频谱检测算法的性能大幅下降问题，提出了一种抗SSDF攻击干扰的协作式频谱检测算法。将SSDF攻击干扰分为Ⅰ类和Ⅱ类，通过Dempster-Shafer (D-S)证据理论设置用户权重和判决门限，区分Ⅰ类恶意用户、Ⅱ类恶意用户和可信用户，只使用可信的频谱检测数据进行信息融合，以判决频谱占用的情况、提高检测性能。理论分析与仿真结果表明，该算法能够有效对抗Ⅰ类和Ⅱ类 SSDF的攻击干扰，其检测性能明显优于现有D-S证据融合频谱检测算法。

关键词: 兵器科学与技术, Dempster-Shafer证据理论, 频谱检测, 数据篡改, 信息融合

Abstract: The malicious users forge and get command of the secondary users, which causes the spectrum sensing data falsification (SSDF) to decrease the performance of existing cooperative spectrum sensing algorithm. A cooperative spectrum sensing algorithm against SSDF is proposed. The SSDF attack is divided into Class Ⅰ and Ⅱ. User weight and decision threshold are set to distinguish the malicious users of Class Ⅰ and Ⅱ malicious users, and trusted secondary users by using Dempster-Shafer (D-S) evidence theory. Only trusted spectrum detection data can be used in information fusion to judge the spectrum usage and improve the detection performance. Theoretical analysis and simulated results show that the proposed algorithm can effectively confront SSDF attack interference, and its judgment performance is superior to that of existing D-S evidence fusion spectrum sensing algorithm. Key

Key words: ordnancescienceandtechnology, Dempster-Shaferevidencetheory, spectrumsensing, datafalsification, informationfusion

中图分类号:

TN925⁺.92

苗成林，李彤，吕军，李皓. 基于Dempster-Shafer证据理论与抗频谱感知数据篡改攻击的协作式频谱检测算法[J]. 兵工学报, 2017, 38(12): 2406-2413.

MIAO Cheng-lin, LI Tong, LYU Jun， LI Hao. Cooperative Spectrum Sensing Algorithm against Spectrum Sensing Data Falsification Based on Dempster-Shafer Evidence Theory[J]. Acta Armamentarii, 2017, 38(12): 2406-2413.

参考文献

［1］ Mitola J, Maguire G. Cognitive radio:making software radios more personal［J］. IEEE Personal Communications Magazine, 1999, 6(4): 13-18.
［2］ Ling X, Wu B, Wen H, et al. Adaptive threshold control for energy detection based spectrum sensing in cognitive radios［J］. IEEE Wireless Communication Letters, 2012, 1(5): 448-451.
［3］ Shobana S, Saravanan R, Muthaiah R. Matched filter based spectrum sensing on cognitive radio for OFDM WLANs［J］. International Journal of Engineering & Technology, 2013, 5(1):142-146.
［4］马彬, 方源, 谢显中. 一种基于信号循环平稳特征的抵御恶意模仿主用户攻击协作频谱检测算法［J］. 中国科学:信息科学, 2016,46(6):789-799.
MA Bin, FANG Yuan, XIE Xian-zhong. Robust cooperative spectrum sensing against primary user emulation attacks based on cyclostationarity［J］. Scientia Sinica： Informationis, 2016, 46 (6):789-799. (in Chinese)
［5］ Ghasemi A, Sousa E S. Collaborative spectrum sensing for opportunistic access in fading environments［C］∥ Proceedings of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Washington, DC, US:IEEE, 2005:131-136.
［6］ Chair Z, Varshney P K. Optimal data fusion in multiple sensor detection systems［J］. IEEE Transactions on Aerospace & Electronic Systems, 1986, 22(1):98-101.
［7］ Ma J, Zhao G, Li Y. Soft Combination and detection for cooperative spectrum sensing in cognitive radio networks［J］. IEEE Transactions on Wireless Communications, 2008, 7(11):4502- 4507.
［8］ Visotsky E, Kuffner S, Peterson R. On collaborative detection of TV transmissions in support of dynamic spectrum sharing［C］∥ Proceedings of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Washington, DC, US：IEEE, 2005:338-345.
［9］ Unnikrishnan J, Veeravalli V V. Cooperative sensing for primary detection in cognitive radio［J］. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1):18-27.
［10］吴名, 宋铁成, 胡静,等. 基于变分贝叶斯推断的新型全局频谱协作感知算法［J］. 通信学报, 2016, 37(2):115-123.
WU Ming, SONG Tie-cheng, HU Jing, et al. A novel cooperative global spectrum sensing algorithm based on variational Bayesian inference［J］．Journal of China Institute of Communications, 2016, 37(2): 115-123.(in Chinese)
［11］董占奇. 基于模糊理论的分布式协作谱感知技术研究［J］. 计算机工程与设计, 2013, 34(6):1936-1940.
DONG Zhan-qi. Fuzzy theory based study on distributed cooperative spectrum sensing technology［J］. Computer Engineering and Design, 2013, 34(6):1936-1940. (in Chinese)

［12］赵知劲, 陈京来. 蜂群优化神经网络的频谱感知［J］. 信号处理, 2016, 32(1):77-82.
ZHAO Zhi-jin, CHEN Jing-lai. A neural network spectrum sensing algorithm using bee colony optimization［J］. Journal of Signal Processin, 2016, 32(1):77-82. (in Chinese)

［13］ Peng Q, Zeng K, Wang J, et al. A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context［C］∥ International Symposium on Personal, Indoor and Mobile Radio Communications. Washington, DC, US: IEEE, 2006:1-5.
［14］周亚建, 刘凯, 肖林. 基于D-S证据理论的加权协作频谱检测算法［J］. 通信学报, 2012, 33(12):19-24.
ZHOU Ya-jian, LIU Kai, XIAO Lin. Weighted cooperative spectrum sensing algorithm based on Dempster-Shafer evidence theory［J］. Journal on Communications, 2012, 33(12):19-24. (in Chinese)
［15］ Nguyen-Thanh N, Koo I. Evidence-theory-based cooperative spectrum sensing with efficient quantization method in cognitive radio［J］. IEEE Transactions on Vehicular Technology, 2011, 60(1):185-195.
［16］ Nguyen-Thanh N, Koo I. An enhanced cooperative spectrum sensing scheme based on evidence theory and reliability source evaluation in cognitive radio context［J］. IEEE Communications Letters, 2009, 13(7):492-494.
［17］ Ni X, Chen H, Xie L, et al. Reputation-based hierarchically cooperative spectrum sensing scheme in cognitive radio networks［C］∥IEEE/CIC International Conference on Communications in China. Washington, DC, US：IEEE, 2013:397-402.
［18］ Cai Y, Mo Y, Ota K, et al. Optimal data fusion of collaborative spectrum sensing under attack in cognitive radio networks［J］. IEEE Network, 2014, 28(1):17-23.
［19］龙敏, 吴靓. 认知无线电网络中安全的协作频谱检测算法［J］. 计算机工程与科学, 2016, 38(10):2045-2050.
LONG Min, WU Liang. A secure cooperative spectrum detection algorithm in cognitive radio networks ［J］. Computer Engineering and Science, 2016, 38(10):2045-2050. (in Chinese)

第38卷第12期
2017 年12月兵工学报ACTA
ARMAMENTARIIVol.38No.12Dec.2017

[1]	周伯俊, 于传强，刘志浩，柯冰. 基于高压储能的特种车辆快速起竖技术与验证[J]. 兵工学报, 2022, 43(7): 1488-1497.
[2]	高月光，冯顺山，刘云辉，黄岐. 不同端盖厚度的圆柱形装药壳体破片初速分布[J]. 兵工学报, 2022, 43(7): 1527-1536.
[3]	王博，蒋平，郭波. 基于多源信息融合的航天阀门可靠性评估[J]. 兵工学报, 2022, 43(1): 199-206.
[4]	李洪瑞. 基于神经网络的分布式被动传感器信息融合技术[J]. 兵工学报, 2020, 41(1): 95-101.
[5]	徐昊玮，廉保旺，刘尚波. 基于滑动窗迭代最大后验估计的多源组合导航因子图融合算法[J]. 兵工学报, 2019, 40(4): 807-819.
[6]	闫理跃, 王厚军, 刘震. 一种结合噪声辅助技术和现场数据的模拟电路实时可靠度预测方法[J]. 兵工学报, 2019, 40(2): 326-333.
[7]	娄伟鹏，郑长松，李和言，陈建文，张周立，马源. 高速《Symbol》v湿式换挡离合器排油阀临界开启和关闭压力研究[J]. 兵工学报, 2017, 38(9): 1665-1672.
[8]	王宪成，杨绍卿，马宁，赵文柱. 车辆柴油机缸套动载荷磨损计算模型研究[J]. 兵工学报, 2017, 38(9): 1673-1680.
[9]	孙皓泽，常天庆，王全东，孔德鹏，戴文君. 一种基于分层多尺度卷积特征提取的坦克装甲目标图像检测方法[J]. 兵工学报, 2017, 38(9): 1681-1691.
[10]	张玉燕，孙莎莎，王振春，曹海要，战再吉. 高速载流电枢表面瞬态温度场仿真与测量[J]. 兵工学报, 2017, 38(9): 1692-1698.
[11]	李臣明，宦超，石怀龙. 某箱式火箭炮对面目标分布式杀伤最优火力分配[J]. 兵工学报, 2017, 38(9): 1699-1704.
[12]	雷娟棉，张嘉炜，谭朝明. 小攻角下船尾外形对旋转弹丸马格努斯效应影响的数值研究[J]. 兵工学报, 2017, 38(9): 1705-1715.
[13]	李泽，闫晓鹏，栗苹，郝新红，王建涛. 扫频式干扰对调频多普勒引信的干扰机理研究[J]. 兵工学报, 2017, 38(9): 1716-1722.
[14]	张浩为，谢军伟，张昭建，宗彬锋，陈唐军. 基于混合自适应遗传算法的相控阵雷达任务调度[J]. 兵工学报, 2017, 38(9): 1761-1770.
[15]	李茂，侯海量，朱锡，黄晓明，李典，陈长海，胡年明. 结构间隙对芳纶纤维增强复合装甲结构抗侵彻性能的影响[J]. 兵工学报, 2017, 38(9): 1797-1805.