Identifying Propagation Source in Time-Varying Networks

Identifying the propagation sources of malicious attacks in complex networks plays a critical role in limiting the damage caused by them through the timely quarantine of the sources. However, the temporal variation in the topology of the underlying networks and the ongoing dynamic processes challenge our traditional source identification techniques which are considered in static networks. In this chapter, we introduce an effective approach used in criminology to overcome the challenges. For simplicity, we use rumor source identification to present the approach.

[1]  Ciro Cattuto,et al.  Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks , 2010, PloS one.

[2]  D. Helbing,et al.  The Hidden Geometry of Complex, Network-Driven Contagion Phenomena , 2013, Science.

[3]  Chee Wei Tan,et al.  Rumor source detection with multiple observations: fundamental limits and algorithms , 2014, SIGMETRICS '14.

[4]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[5]  Alessandro Vespignani,et al.  Time varying networks and the weakness of strong ties , 2013, Scientific Reports.

[6]  Matheus Palhares Viana,et al.  On time-varying collaboration networks , 2013, J. Informetrics.

[7]  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.

[8]  Xin Jiang,et al.  Identify the diversity of mesoscopic structures in networks: A mixed random walk approach , 2013 .

[9]  Pan Hui,et al.  Optimal Distributed Malware Defense in Mobile Networks with Heterogeneous Devices , 2014, IEEE Transactions on Mobile Computing.

[10]  Mahmoud Fouz,et al.  Why rumors spread so quickly in social networks , 2012, Commun. ACM.

[11]  Sergio Gómez,et al.  Modeling Epidemic Spreading in Complex Networks: Concurrency and Traffic , 2012 .

[12]  Yamir Moreno,et al.  Dynamics of rumor spreading in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[14]  Jafar Adibi,et al.  The Enron Email Dataset Database Schema and Brief Statistical Report , 2004 .

[15]  Andrea Baronchelli,et al.  Quantifying the effect of temporal resolution on time-varying networks , 2012, Scientific Reports.

[16]  Devavrat Shah,et al.  Detecting sources of computer viruses in networks: theory and experiment , 2010, SIGMETRICS '10.

[17]  Lei Ying,et al.  Information source detection in the SIR model: A sample path based approach , 2013, ITA.

[18]  Martin Vetterli,et al.  Locating the Source of Diffusion in Large-Scale Networks , 2012, Physical review letters.

[19]  Wuqiong Luo,et al.  Identifying Infection Sources and Regions in Large Networks , 2012, IEEE Transactions on Signal Processing.