基于模糊逻辑的智能电网中恶意节点的检测算法
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摘要
智能电网是以无线通信方式传输数据,而无线通信网络容易遭受多类网络攻击。为此,提出基于模糊逻辑信任模型(FLTM),检测智能电网中的恶意节点。将直接信任、间接信任和历史信任作为模糊系统的输入,将其输出作为节点的信任值,进而检测恶意节点。实验数据表明,利用FLTM模型提高了对恶意节点的检测率。
The smart grids transmit data in the manner of wireless communication. However,the wireless communication network is vulnerable to various cyber-attacks. The trust model is considered as an important mode of preventing the large-scale communication network from malicious cyber-attacks. Therefore,a fuzzy logic trust model(FLTM)is proposed to detect malicious nodes in smart grids. The direct trust,indirect trust and past trust are taken as the input of the fuzzy system,and the output of the fuzzy system is considered to be trust values of nodes,so as to detect malicious nodes. The experimental data shows that the FLTM can improve the detection rate of malicious nodes.
The smart grids transmit data in the manner of wireless communication. However,the wireless communication network is vulnerable to various cyber-attacks. The trust model is considered as an important mode of preventing the large-scale communication network from malicious cyber-attacks. Therefore,a fuzzy logic trust model(FLTM)is proposed to detect malicious nodes in smart grids. The direct trust,indirect trust and past trust are taken as the input of the fuzzy system,and the output of the fuzzy system is considered to be trust values of nodes,so as to detect malicious nodes. The experimental data shows that the FLTM can improve the detection rate of malicious nodes.
引文
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[2] PANTAZIS N A,NIKOLIDAKIS S A,VERGADOS D D. Energy-efficient routing protocols in wireless sensor networks:a survey[J]. IEEE communications surveys&tutorials,2013,15(2):551-591.
[3] JIANG J,SUN H. Performance assessment of distributed communication architectures in smart grid[C]//Proceedings of2016 IEEE 83rd Vehicular Technology Conference. Nanjing:IEEE,2016:1-5.
[4] LI X,ZHOU F,DU J. LDTS:a lightweight and dependable trust system for clustered wireless sensor networks[J]. IEEE transactions on information forensics and security, 2013, 8(6):924-935.
[5] JIANG J,HAN G,WANG F,et al. An efficient distributed trust model for wireless sensor networks[J]. IEEE transactions on parallel and distributed systems,2015,26(5):1228-1237.
[6] LIAO H,ZHU H. An energy balanced clustering algorithm based on LEACH protocol[J]. Applied mechanics and materials,2013,341:1138-1143.
[7] MARCHANG N,DATTA R. Light-weight trust-based routing protocol for mobile ad hoc networks[J]. IET information security,2012,6(2):77-83.
[8] XIANG M,LIU W,BAI Q. Trust-based geographical routing for smart grid communication networks[C]//Proceedings of2012 IEEE Third International Conference on Smart Grid Communications.[S.l.]:IEEE,2012:704-709.
[9] NIYATO D,HOSSAIN E,FALLAHI A. Sleep and wakeup strategies in solar-powered wireless sensor/mesh networks:performance analysis and optimization[J]. IEEE transactions on mobile computing,2007,6(2):221-236.
[10] YANG L,LU Y,ZHONG Y,et al. A multi-hop energy neutral clustering algorithm for maximizing network information gathering in energy harvesting wireless sensor networks[J].Sensors,2015,16(1):1-22.
[11] DESHMUKH R,DESHMUKH R,SHARMA M. Rule-based and cluster-based intrusion detection technique for wireless sensor network[J]. International journal of computer science and mobile computing,2013,2(6):200-208.
[12] LABRAOUI N. A reliable trust management scheme in wireless sensor networks[C]//Proceedings of 2015 12th International Symposium on Programming and Systems. Algiers:IEEE,2015:1-6.