基于时滞的无线传感网恶意软件传播模型
|
摘要
为了深入研究无线传感网中恶意软件程序的传播过程,探讨存在时间滞留的情况下恶意软件的传播模型并分析其传播行为,建立了基于时滞的无线传感网恶意软件传播模型。该模型引入传染病学理论,结合传感器节点能量有限、存在时延等特性,对传播过程和特征进行了具体的分析,并对模型系统中状态转换关系、平衡点的存在性以及局部和全局稳定性进行了正确性和完备性分析证明。基于理论分析,建立仿真模型,设置相应的传播比率,对传播过程中各状态节点数量的变化进行仿真实验,进行了系统稳定性分析;对传播过程中时滞的取值和变化进行仿真,进行了系统时滞影响的分析和阈值的确定;与具有代表性的稳定的延迟网络蠕虫传播模型进行对比,给出了不同传播模型的数据分析。实验验证了稳定性理论分析的正确性,确定了影响传播稳定性的参数阈值及其对传播的影响,与其他模型进行了比较,分析了时滞对系统的影响。提出的模型为恶意软件的传播和控制提供了分析和研究思路,对于建立遏制恶意程序在移动无线传感网中扩散传播的安全策略具有指导意义。
In order to deeply study the propagation process of malicious software programs in wireless sensor networks, discuss propagation models of malicious software in the presence of time lag and analyze their propagation behaviors, a delay-based malicious software propagation model for wireless sensor networks was established based on the existing research on the security issues of wireless sensor networks. Based on the theory of infectious diseases and the characteristics of sensor nodes such as limited energy and delay, the dissemination process and characteristics of sensor nodes were analyzed in detail. The correctness and completeness of the state transition relationship, the existence of equilibrium point, the local stability and global stability in the model system were analyzed and proved. Based on theoretical analysis, a simulation model was established,in which the corresponding propagating ratio was set up, the simulation experiments were carried out on the changes of the number of nodes in each state during the propagating process, and the stability analysis of the system was provided. The values and changes of the time delay in the propagating process were simulated, and the influence of the time delay and the determination of the threshold were analyzed. At the same time,the comparative analysis of different propagation models was provided between the typical stable delay network worm propagation models and the proposed model. Experiments verified the validity of the theoretical stability analysis, determined the parameter threshold which affects the stability of the propagation and its impact on the propagation. The proposed model was compared with other models, and the influence of time delay on the system was analyzed. The proposed model has provided the analysis and research ideas for the spread and control of malicious software and has the guiding significance for the establishment of a security strategy to curb the spread of malicious programs in mobile wireless sensor networks.
In order to deeply study the propagation process of malicious software programs in wireless sensor networks, discuss propagation models of malicious software in the presence of time lag and analyze their propagation behaviors, a delay-based malicious software propagation model for wireless sensor networks was established based on the existing research on the security issues of wireless sensor networks. Based on the theory of infectious diseases and the characteristics of sensor nodes such as limited energy and delay, the dissemination process and characteristics of sensor nodes were analyzed in detail. The correctness and completeness of the state transition relationship, the existence of equilibrium point, the local stability and global stability in the model system were analyzed and proved. Based on theoretical analysis, a simulation model was established,in which the corresponding propagating ratio was set up, the simulation experiments were carried out on the changes of the number of nodes in each state during the propagating process, and the stability analysis of the system was provided. The values and changes of the time delay in the propagating process were simulated, and the influence of the time delay and the determination of the threshold were analyzed. At the same time,the comparative analysis of different propagation models was provided between the typical stable delay network worm propagation models and the proposed model. Experiments verified the validity of the theoretical stability analysis, determined the parameter threshold which affects the stability of the propagation and its impact on the propagation. The proposed model was compared with other models, and the influence of time delay on the system was analyzed. The proposed model has provided the analysis and research ideas for the spread and control of malicious software and has the guiding significance for the establishment of a security strategy to curb the spread of malicious programs in mobile wireless sensor networks.
引文
[1]Akyildiz I F,Su W,Sankarasubranabiam Y,et al.Wireless sensor networks:A survey[J].Computer Networks,2002,38(4):393-422.
[2]Cao Yulin,Wang Xiaoming,He Zaobo.Optimal security strategy for malware propagation in mobile wireless sensor networks[J].Chinese Journal of Electronics,2016,44(8):1851-1857.[曹玉林,王小明,何早波.移动无线传感网中恶意软件传播的最优安全策略[J].电子学报,2016,44(8):1851-1857.]
[3]Yao Yu,Feng Xiaodong,Yang Wei,et al.Analysis of a delayed Internet worm propagation model with impulsive quarantine strategy[J].Mathematical Problems in Engineer-ing,2014,2014:Article ID 369360.
[4]Yao Yu,Xie Xiaowu,Guo Hao,et al.Hopf bifurcation in an Internet worm propagation model with time delay in quarantine[J].Mathmatical and Computer Modeling,2013,57(11/12):2635-2646.
[5]Giannetsos T,Dimitriou T,Krontiris I,et al.Arbitrary code injection through self-propagating worms in Von Neumann architecture devices[J].The Computer Journal,2010,53(10):1576-1593.
[6]Gu Qijun,Ferguson C,Noorani R.A study of self-propagating mal-packets in sensor networks:Attacks and defenses[J].Computers&Security,2011,30(1):13-27.
[7]Wang Yini,Wen Sheng Xiang Yang,et al.Modeling the propagation of worms in networks:A survey[J].IEEE Communications Surveys&Tutorials,2014,16(2):942-960.
[8]De P,Liu Yonghe,Das S K.Modeling node compromise spread in wireless sensor networks using epidimic theory[C]//Proceedings of the 2006 International Symposium on the World of Wireless,Mobile and Multimedia Networks(WoWMoM 2006).Buffalo:IEEE,2006:237-243.
[9]Khouzani M H R,Sarkar S.Maximum damage battery depletion attack in mobile sensor networks[J].IEEE Transactions on Automatic Control,2011,56(10):2358-2368.
[10]Hachinyan O.Detection of malicious software on based on multiple equations of API-calls sequences[C]//Proceedings of the 2017 IEEE Conference of Russian Young Researchers in Electrical&Electronic Engineering.St.Petersburg:IEEE,2017:415-418.
[11]Karyotis V.Markov random fields for malware propagation:The case of chain networks[J].IEEE Communications Letters,2010,14(9):875-877.
[12]Song Yurong,Jiang Guoping.Modeling malware propagation in wireless sensor networks using cellular automata[C]//Proceedings of the 2008 International Conference on Neural Networks and Signal Processing.Nanjing:IEEE,2008:623-627.
[13]Rashed A,Mahapatra R N.The three-tier security scheme in wireless sensor network with mobile sinks[J].IEEE Transactions on Parallel and Distributed System,2012,23(5):958-965.
[14]Zeng Rong.Malware propagation and inhibition in vehicular Ad-hoc networks[D].Harbin:Harbin Institute of Technology,2018.[曾蓉.车载自组网恶意软件传播及其抑制[D].哈尔滨:哈尔滨工业大学,2018.]
[15]Liu Wanping,Liu Chao,Yang Zheng,et al.Modeling the propagation of mobile malware on complex networks[J].Communications in Nonlinear Science and Numerical Simulation,2016,37:249-264.
[2]Cao Yulin,Wang Xiaoming,He Zaobo.Optimal security strategy for malware propagation in mobile wireless sensor networks[J].Chinese Journal of Electronics,2016,44(8):1851-1857.[曹玉林,王小明,何早波.移动无线传感网中恶意软件传播的最优安全策略[J].电子学报,2016,44(8):1851-1857.]
[3]Yao Yu,Feng Xiaodong,Yang Wei,et al.Analysis of a delayed Internet worm propagation model with impulsive quarantine strategy[J].Mathematical Problems in Engineer-ing,2014,2014:Article ID 369360.
[4]Yao Yu,Xie Xiaowu,Guo Hao,et al.Hopf bifurcation in an Internet worm propagation model with time delay in quarantine[J].Mathmatical and Computer Modeling,2013,57(11/12):2635-2646.
[5]Giannetsos T,Dimitriou T,Krontiris I,et al.Arbitrary code injection through self-propagating worms in Von Neumann architecture devices[J].The Computer Journal,2010,53(10):1576-1593.
[6]Gu Qijun,Ferguson C,Noorani R.A study of self-propagating mal-packets in sensor networks:Attacks and defenses[J].Computers&Security,2011,30(1):13-27.
[7]Wang Yini,Wen Sheng Xiang Yang,et al.Modeling the propagation of worms in networks:A survey[J].IEEE Communications Surveys&Tutorials,2014,16(2):942-960.
[8]De P,Liu Yonghe,Das S K.Modeling node compromise spread in wireless sensor networks using epidimic theory[C]//Proceedings of the 2006 International Symposium on the World of Wireless,Mobile and Multimedia Networks(WoWMoM 2006).Buffalo:IEEE,2006:237-243.
[9]Khouzani M H R,Sarkar S.Maximum damage battery depletion attack in mobile sensor networks[J].IEEE Transactions on Automatic Control,2011,56(10):2358-2368.
[10]Hachinyan O.Detection of malicious software on based on multiple equations of API-calls sequences[C]//Proceedings of the 2017 IEEE Conference of Russian Young Researchers in Electrical&Electronic Engineering.St.Petersburg:IEEE,2017:415-418.
[11]Karyotis V.Markov random fields for malware propagation:The case of chain networks[J].IEEE Communications Letters,2010,14(9):875-877.
[12]Song Yurong,Jiang Guoping.Modeling malware propagation in wireless sensor networks using cellular automata[C]//Proceedings of the 2008 International Conference on Neural Networks and Signal Processing.Nanjing:IEEE,2008:623-627.
[13]Rashed A,Mahapatra R N.The three-tier security scheme in wireless sensor network with mobile sinks[J].IEEE Transactions on Parallel and Distributed System,2012,23(5):958-965.
[14]Zeng Rong.Malware propagation and inhibition in vehicular Ad-hoc networks[D].Harbin:Harbin Institute of Technology,2018.[曾蓉.车载自组网恶意软件传播及其抑制[D].哈尔滨:哈尔滨工业大学,2018.]
[15]Liu Wanping,Liu Chao,Yang Zheng,et al.Modeling the propagation of mobile malware on complex networks[J].Communications in Nonlinear Science and Numerical Simulation,2016,37:249-264.