SADI: A Novel Model to Study the Propagation of Social Worms in Hierarchical Networks

As more and more people rely on social networks for business and life, social worms constitute one of the major security threats to our society. Modern social worms exhibit two new features, message notification and the temporal characteristic of human mobility. Message notification indicates a user will get a reminder once a new message comes to a social account. The temporal characteristic of human mobility indicates a user can operate corresponding computer in different locations with different resting time. Previous scholars have proposed some analytical models for the propagation dynamics of social worms. However, they did not consider the above two features and there is one critical problem unrealized, which is structural imperfection of network topology. Previous models have not taken into account the hierarchical topology structure, which results from a many-to-many relationship between users and hosts. To address these problems, we model propagation dynamics of social worms oriented hierarchical networks in this paper, and the proposed model accurately describes the propagation behavior of social worms. We conduct both a theoretical analyses and extensive simulations to show our model can overcome inaccuracy in the number of infected nodes and provide a stronger approximation for the worm propagation. The results show that our model presented in this paper achieves a greater accuracy in characterizing the propagation of modern social worms.

[1]  EschelbeckGerhard The Laws of Vulnerabilities , 2005 .

[2]  Sencun Zhu,et al.  A Social Network Based Patching Scheme for Worm Containment in Cellular Networks , 2009, IEEE INFOCOM 2009.

[3]  Xiao Liang,et al.  Unraveling the origin of exponential law in intra-urban human mobility , 2012, Scientific Reports.

[4]  Vinod Yegneswaran,et al.  PathCutter: Severing the Self-Propagation Path of XSS JavaScript Worms in Social Web Networks , 2012, NDSS.

[5]  Benjamin Livshits,et al.  Spectator: Detection and Containment of JavaScript Worms , 2008, USENIX Annual Technical Conference.

[6]  Tianbo Wang,et al.  The Temporal Characteristic of Human Mobility: Modeling and Analysis of Social Worm Propagation , 2015, IEEE Communications Letters.

[7]  Xianfeng Huang,et al.  Understanding metropolitan patterns of daily encounters , 2013, Proceedings of the National Academy of Sciences.

[8]  Yacine Challal,et al.  A new worm propagation threat in BitTorrent: modeling and analysis , 2010, Telecommun. Syst..

[9]  Alessandro Vespignani,et al.  Epidemic spreading in complex networks with degree correlations , 2003, cond-mat/0301149.

[10]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[11]  Jeffrey O. Kephart,et al.  Directed-graph epidemiological models of computer viruses , 1991, Proceedings. 1991 IEEE Computer Society Symposium on Research in Security and Privacy.

[12]  Xinwen Fu,et al.  Self-Disciplinary Worms and Countermeasures: Modeling and Analysis , 2010, IEEE Transactions on Parallel and Distributed Systems.

[13]  Jun Zhang,et al.  Modeling Propagation Dynamics of Social Network Worms , 2013, IEEE Transactions on Parallel and Distributed Systems.

[14]  Stephanie Forrest,et al.  Email networks and the spread of computer viruses. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Chuanyi Ji,et al.  Spatial-temporal modeling of malware propagation in networks , 2005, IEEE Transactions on Neural Networks.

[16]  Guanhua Yan,et al.  Malware propagation in online social networks: nature, dynamics, and defense implications , 2011, ASIACCS '11.

[17]  W. Fan,et al.  Assembly effect of groups in online social networks , 2013 .

[18]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[19]  Ning Zhong,et al.  Network immunization and virus propagation in email networks: experimental evaluation and analysis , 2010, Knowledge and Information Systems.

[20]  Alessandro Vespignani,et al.  Epidemic dynamics in finite size scale-free networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[22]  Xiao Liang,et al.  The scaling of human mobility by taxis is exponential , 2011, ArXiv.

[23]  Zhendong Su,et al.  Client-Side Detection of XSS Worms by Monitoring Payload Propagation , 2009, ESORICS.

[24]  Vasileios Karyotis,et al.  Markov Random Fields for Malware Propagation: The Case of Chain Networks , 2010, IEEE Communications Letters.

[25]  Donald F. Towsley,et al.  Modeling and Simulation Study of the Propagation and Defense of Internet E-mail Worms , 2007, IEEE Transactions on Dependable and Secure Computing.

[26]  Uyen Trang Nguyen,et al.  A Study of XSS Worm Propagation and Detection Mechanisms in Online Social Networks , 2013, IEEE Transactions on Information Forensics and Security.

[27]  Seungyeop Han,et al.  Analysis of topological characteristics of huge online social networking services , 2007, WWW '07.

[28]  Hisashi Tanizaki,et al.  Computational methods in statistics and econometrics , 2004 .

[29]  Michalis Faloutsos,et al.  Information Survival Threshold in Sensor and P2P Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[30]  A. F. Pacheco,et al.  Epidemic incidence in correlated complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Guanhua Yan,et al.  Modeling Propagation Dynamics of Bluetooth Worms (Extended Version) , 2009, IEEE Transactions on Mobile Computing.

[32]  Wanlei Zhou,et al.  Locating Defense Positions for Thwarting the Propagation of Topological Worms , 2012, IEEE Communications Letters.

[33]  Xun Wang,et al.  Modeling and Detection of Camouflaging Worm , 2011, IEEE Transactions on Dependable and Secure Computing.