目标成本值最优的物联网WSS蠕虫抑制算法
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摘要
物联网无线服务系统(wireless service system,WSS)是以通用的协议标准实现人与物、物与物相连的实时网络交互系统.该系统在设备中嵌入无线传感器节点以实现数据上传和决策下发,但传感器节点的同构性特点使得蠕虫传播问题日益严重.为此,在对现有蠕虫传播的流行病模型进行分类并总结各类模型特点的基础上,首先提出了具有睡眠状态和隔离状态的流行病模型,定义了系统中节点的状态转换关系;其次,依据节点的射频通信距离,确定了具有实际传染能力的感染节点数量及范围;再次,引入蠕虫与无线服务系统的目标成本函数,给出了基于目标成本值的完全信息动态微分博弈模型;然后,证明了该博弈存在鞍点策略,利用状态变量、协状态变量和汉密尔顿函数求解鞍点策略并设计了保证目标成本值最优的防御策略算法;最后,仿真实现本算法与2种蠕虫防御策略算法,通过各状态节点的变化特点及目标成本值的对比实验进行性能评估.实验结果表明:基于改进流行病模型的最优防御算法在抑制无线服务系统蠕虫传播方面有明显优势.
With the adoption of the general standard of communication protocol,wireless service system(WSS)in IoT is proposed to achieve the real-time connection between person and things or things and things.According to the characteristics of IEEE 802.15.4,wireless sensor nodes are embedded in the devices for data collection and command broadcasting.However,the isomorphism of the sensor nodes makes the worm propagation an increasingly serious problem.Firstly,based on the classification of epidemiological models related to worm propagation and the analysis of the characteristics of various models,an epidemiological model is constructed,which specially introduces sleep state and quarantine state into state transition.The transition relationship of nodes is defined simultaneously.Secondly,according to the radio frequency,the number and range of infected nodes with actual transmission ability are determined.Thirdly,we introduce the target cost function between worm and wireless service system,and put forward a dynamic differential game with complete information based on the overall damage.Then,the existence of saddle-point solution is proved,which is solved by combining state parameters,cooperative state variables and Hamiltonian functions.The optimal defense algorithm is proposed to minimize target cost function.Finally,different algorithms are implemented and the performance evaluation is carried out by comparing the characteristics of nodes in each state and the corresponding overall damage.The experimental results show that the optimal defense algorithm based on improved epidemic model can suppress worm propagation in wireless service system effectively and efficiently.
With the adoption of the general standard of communication protocol,wireless service system(WSS)in IoT is proposed to achieve the real-time connection between person and things or things and things.According to the characteristics of IEEE 802.15.4,wireless sensor nodes are embedded in the devices for data collection and command broadcasting.However,the isomorphism of the sensor nodes makes the worm propagation an increasingly serious problem.Firstly,based on the classification of epidemiological models related to worm propagation and the analysis of the characteristics of various models,an epidemiological model is constructed,which specially introduces sleep state and quarantine state into state transition.The transition relationship of nodes is defined simultaneously.Secondly,according to the radio frequency,the number and range of infected nodes with actual transmission ability are determined.Thirdly,we introduce the target cost function between worm and wireless service system,and put forward a dynamic differential game with complete information based on the overall damage.Then,the existence of saddle-point solution is proved,which is solved by combining state parameters,cooperative state variables and Hamiltonian functions.The optimal defense algorithm is proposed to minimize target cost function.Finally,different algorithms are implemented and the performance evaluation is carried out by comparing the characteristics of nodes in each state and the corresponding overall damage.The experimental results show that the optimal defense algorithm based on improved epidemic model can suppress worm propagation in wireless service system effectively and efficiently.
引文
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[29] Yu Shui,Gu Guofei,Barnawi A,et al.Malware propagation in large-scale networks[J]. IEEE Transactions on Knowledge and Data Engineering,2015,27(1):170-179
[2] He Wu,Yan Gongjun,Xu Lida.Developing vehicular data cloud services in the IoT environment[J]. IEEE Transactions on Industrial Informatics,2014,10(2):1587-1595
[3] Gope P,Hwang T.BSN-Care:A secure IoT-based modern healthcare system using body sensor network[J].IEEE Sensors Journal,2016,16(5):1368-1376
[4] Hoff E,Laursen B,Bridges K.Security and privacy in Internet of things:Methods,architectures,and solutions[J].Security and Communication Networks,2014,7(11):2681-2682
[5] Zhang Yuanyu,Shen Yulong,Wang Hua,et al.On secure wireless communications for IoT under eavesdropper collusion[J].IEEE Transactions on Automation Science and Engineering,2016,13(3):1281-1293
[6] Xu Qian, Ren Pinyi, Song Houbing,et al. Security enhancementforIoTcommunicationsexposedto eavesdroppers with uncertain locations[J].IEEE Access,2016,18(4):2840-2853
[7] Pa Y M P,Suzuki S,Yoshioka K,et al.IoTPOT:A novel honeypot for revealing current IoT threats[J].Journal of Information Processing,2016,24(3):522-533
[8] Wang Fangwei,Zhang Yunkai,Wang Changguang,et al.Stability analysis of an e-SEIAR model with point-to-group worm propagation[J].Communications in Nonlinear Science and Numerical Simulation,2015,20(3):897-904
[9] Wang Xu, Ni Wei, Zheng Kangfeng, et al. Virus propagation modeling and convergence analysis in large-scale networks[J].IEEE Transactions on Information Forensics and Security,2016,11(10):2241-2254
[10] Tong Xiaojun,Zhang Miao,Wang Zhu.The cost optimal control system based on the Kermack–Mckendrick worm propagationmodel[J]. JournalofAlgorithms&Computational Technology,2016,10(2):82-89
[11] Chen Zhe,Ji Chuanyi.Spatial-temporal modeling of malware propagation in networks[J].IEEE Transactions on Neural Networks,2010,16(5):1291-1303
[12] Khanh N H.Dynamics of a worm propagation model with quarantine in wireless sensor networks[J]. Applied Mathematics&Information Sciences,2016,10(5):1739-1746
[13] Bradley J T,Gilmore S T,Hillston J.Analyzing distributed Internetwormattacksusingcontinuousstate-space approximation of process algebra models[J].Journal of Computer and System Science,2008,74(6):1013-1032
[14] Ma Xiaolong,Liu Hui,Zhang Jing.Analysis of the impact of heterogeneous network environment on worm propagation[C]//Proc of the 3rd Int Conf on Multimedia Information Networking&Security.Piscataway,NJ:IEEE,2011:457-462
[15] Zhu Kenan,Yin Baolin, Mao Yaming,et al. Malware classification approach based on valid window and na6ve Bayes[J].Joural of Computer Research and Development,2014,51(2):373-381(in Chinese)(朱克楠,尹宝林,冒亚明,等.基于有效窗口和朴素贝叶斯的恶意代码分类[J].计算机研究与发展,2014,51(2):373-381)
[16] Mao Weixuan,Cai Zhongmin,Tong Li.Malware detection method based on active learning[J].Journal of Software,2017,28(2):384-397(in Chinese)(毛蔚轩,蔡忠闽,童力.一种基于主动学习的恶意代码检测方法[J].软件学报,2017,28(2):384-397)
[17] Zou Changchun, Gong Weibo, Towsley D. On the performance of Internet worm scanning strategies[J].Journal of Performance Evaluation,2006,63(7):700-723
[18] Ma Hongliang,Wang Wei,Han Zhen.Detecting and deobfuscating obfuscated malicious JavaScript code[J].Chinese Journal of Computers,2017,40(7):1699-1713(in Chinese)(马洪亮,王伟,韩臻.混淆恶意JavaScript代码的检测与反混淆方法研究[J].计算机学报,2017,40(7):1699-1713)
[19] Das S,Liu Yang,Zhang Wei,et al.Semantics-based online malware detection:Towards efficient real-time protection against malware[J].IEEE Transactions on Information Forensics and Security,2016,11(2):289-302
[20] Guo Yanghe,Ten C W, Hu Shiyan,et al.Preventive maintenance for advanced metering infrastructure against malware propagation[J].IEEE Transactions on Smart Grid,2016,7(3):1314-1328
[21] Liu Bo,Wang Huaimin,Xiao Fengtao,et al.EnhancedAAWP,a heterogeneous network oriented worm propagation model[J].Journal on Communications,2011,32(12):103-113(in Chinese)(刘波,王怀民,肖枫涛,等.面向异构网络环境的蠕虫传播模型Enhanced-AAWP[J].通信学报,2011,32(12):103-113)
[22] Saracino A,Sgandurra D,Dini G,et al.Madam:Effective and efficient behavior-based android malware detection and prevention[J].IEEE Transactions on Dependable and Secure Computing,2018,15(1):83-97
[23] Theodoros S,Konstantinos M,Alexios M,et al.A game theoretical method for cost-benefit analysis of malware dissemination prevention[J].Information Systems Security,2015,24(6):164-176
[24] Mishra B K, Pandey S K. Dynamic model of worm propagation in computer network[J].Applied Mathematical Modelling,2014,38(7):2173-2179
[25] Pandey S K,Samanta B.Malware attack through botnet in e-commerce network:A dynamic model[J].International Journal Series in Engineering Science,2016,2(3):13-25
[26] Sarah S,Ness B S,Saurabh B.Modeling and automated containment ofworms[J]. IEEETransactionson Dependable and Secure Computing,2008,5(2):528-537
[27] Liu Bo,Zhou Wei,Gao Lei,et al.Malware propagations in wireless ad hoc networks[J].IEEE Transactions on Dependable and Secure Computing,2016,34(4):1365-1376
[28] Chen Pinyu,Cheng Shiming,Chen Kangcheng.Optimal control of epidemic information dissemination over networks[J].IEEE Transactions on Cybernetics,2014,44(12):2316-2328
[29] Yu Shui,Gu Guofei,Barnawi A,et al.Malware propagation in large-scale networks[J]. IEEE Transactions on Knowledge and Data Engineering,2015,27(1):170-179