基于微分博弈的在线社交网络恶意程序传播优化控制方法

doi:10.11959/j.issn.1000-0801.2015215

Abstract

Abstract:

Online social network（OSN）is prone to propagating malware because of their special characteristics.By developing traditional epidemic theory and considering effort intensities of the defender and malware，a differential-equation model to suitably describe characteristics of OSN malware was proposed.Using the differential game，a malware-defense game was constructed to reflect interactions between the defender and malware.Thus，optimal dynamic control strategies for the defender were given when the malware dynamically changed its optimal control strategies.Numerical experiments show that the proposed method is obviously able to suppress the malware propagation.Therefore，a novel way to defend OSN malware was provided.

Key words: online social network, malware, epidemic theory, differential game

Shigen Shen,Longjun Huang,Keli Hu,Hongjie Li,Risheng Han,Qiying Cao. Differential Game-Based Optimal Control Method for Preventing Malware Propagation in Online Social Network[J]. Telecommunications Science, 2015, 31(10): 66-73.

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References 29

1	Wang Y ， Wen S ， Xiang Y ， et al. Modeling the propagation of worms in networks：a survey. IEEE Communications Surveys and Tutorials， 2014，16（2）： 942～960
2	Gao H ， Hu J ， Huang T ， et al. Security issues in online social networks. IEEE Internet Computing， 2011，15（4）： 56～63
3	Makridakis A ， Athanasopoulos E ， Antonatos S ， et al. Understanding the behavior of malicious applications in social networks. IEEE Network， 2010，24（5）： 14～19
4	Chen J ， Kiremire A R ， Brust M R ， et al. Modeling online social network users’ profile attribute disclosure behavior from a game theoretic perspective. Computer Communications， 2014（49）： 18～32
5	刘昕，贾春福，石乐义等. 基于社会计算的IM恶意代码防御机制. 电子学报 2013，41（6）： 1130～1139 Liu X ， Jia C F ， Shi L Y ， et al. A collaborative defending scheme based on social computing against IM malicious codes. Acta Electronica Sinica， 2013，41（6）： 1130～1139
6	Bose A ， Shin K G . Agent-based modeling of malware dynamics in heterogeneous environments. Security and Communication Networks， 2013，6（12）： 1576～1589
7	Faghani M R ， Nguyen U T . A study of xss worm propagation and detection mechanisms in online social networks. IEEE Transactions on Information Forensics and Security， 2013，8（11）： 1815～1826
8	Sanzgiri A ， Hughes A ， Upadhyaya S . Analysis of malware propagation in Twitter. Proceedings of the IEEE Symposium on Reliable Distributed Systems， Braga，Portugal， 2013： 195～204
9	Feng L ， Liao X ， Han Q ， et al. Dynamical analysis and control strategies on malware propagation model. Applied Mathematical Modeling， 2013，37（16～17）： 8225～8236
10	Khosroshahy M ， Mehmet Ali M K ， Qiu D . The SIC botnet lifecycle model：a step beyond traditional epidemiological models. Computer Networks， 2013，57（2）： 404～421
11	Adu-Gyamfi D ， Wang Y ， Zhang F ， et al. A model for spreading behavior of passive worms in mobile social networks. Journal of Computational Information Systems， 2014，10（7）： 2667～2675
12	Yu S ， Gu G ， Barnawi A ， et al. Malware propagation in large-scale networks. IEEE Transactions on Knowledge and Data Engineering， 2015，27（1）： 170～179
13	Cheng S M ， Ao W C ， Chen P Y ， et al. On modeling malware propagation in generalized social networks. IEEE Communications Letters， 2011，15（1）： 25～27
14	Karyotis V ， Papavassiliou S . Macroscopic malware propagation dynamics for complex networks with churn. IEEE Communications Letters， 2015，19（4）： 577～580
15	Peng S ， Yu S ， Yang A . Smartphone malware and its propagation modeling：a survey. IEEE Communications Surveys and Tutorials， 2014，16（2）： 925～941
16	Peng S ， Wang G ， Yu S . Modeling the dynamics of worm propagation using two-dimensional cellular automata in smartphones. Journal of Computer and System Sciences， 2013，79（5）： 586～595
17	Peng S ， Wu M ， Wang G ， et al. Propagation model of smartphone worms based on semi-Markov process and social relationship graph. Computers and Security， 2014（44）： 92～103
18	Peng S ， Wu M ， Wang G ， et al. Containing smartphone worm propagation with an influence maximization algorithm. Computer Networks， 2014（74）： 103～113
19	Wen S ， Zhou W ， Zhang J ， et al. Modeling propagation dynamics of social network worms. IEEE Transactions on Parallel and Distributed Systems， 2013，24（8）： 1633～1643
20	杨海陆，张健沛，杨静 . 基于社区的移动互联网混合蠕虫双向反馈遏制系统. 计算机研究与发展 2014，51（2）： 311～324 Yang H L ， Zhang J P ， Yang J . Community-based bidirectional feedback system for hybrid worm containment in mobile internet Journal of Computer Research and Development， 2014，51（2）： 311～324
21	刘玉岭，冯登国，吴丽辉等. 基于静态贝叶斯博弈的蠕虫攻防策略绩效评估. 软件学报， 2012，23（3）： 712～723 Liu Y L ， Feng D G ， Wu L H ， et al. Performance evaluation of worm attack and defense strategies based on static Bayesian gam. Journal of Software， 2012，23（3）： 712～723
22	Wen S ， Zhou W ， Wang Y ， et al. Locating defense positions for thwarting the propagation of topological worms. IEEE Communications Letters， 2012，16（4）： 560～563
23	Ramachandran K K ， Sikdar B . Dynamics of malware spread in decentralized peer-to-peer networks. IEEE Transactions on Dependable and Secure Computing， 2011，8（4）： 617～623
24	Theodorakopoulos G ， Baras J S ， Le Boudec J Y . Dynamic network security deployment under partial information. Proceedings of the 46th Annual Allerton Conference on Communication，Control，and Computing， Monticello，USA， 2008： 261～267
25	Omic J ， Orda A ， Van Mieghem P . Protecting against network infections：a game theoretic perspective. Proceedings of IEEE INFOCOM， Rio de Janeiro，Brazil， 2009： 1485～1493
26	Bensoussan A ， Kantarcioglu M ， Hoe S . A game-theoretical approach for finding optimal strategies in a botnet defense model. Proceedings of GameSec 2010， Berlin，Germany， 2010： 135～148
27	Khouzani M H R ， Sarkar S ， Altman E . Saddle-point strategies in malware attack. IEEE Journal on Selected Areas in Communications， 2012，30（1）： 31～43
28	Aumann R J ， Hart S . Handbook of Game Theory，Amsterdam：Elsevier， 1994
29	Bressan A . Noncooperative differential games. Milan Journal of Mathematics， 2011，79（2）： 357～427

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Differential Game-Based Optimal Control Method for Preventing Malware Propagation in Online Social Network

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