Robustness of non-interdependent and interdependent networks against dependent and adaptive attacks

Abstract Robustness of complex networks has been extensively studied via the notion of site percolation, which typically models independent and non-adaptive attacks (or disruptions). However, real-life attacks are often dependent and/or adaptive. This motivates us to characterize the robustness of complex networks, including non-interdependent and interdependent ones, against dependent and adaptive attacks. For this purpose, dependent attacks are accommodated by L -hop percolation where the nodes within some L -hop ( L ≥ 0 ) distance of a chosen node are all deleted during one attack (with L = 0 degenerating to site percolation). Whereas, adaptive attacks are launched by attackers who can make node-selection decisions based on the network state in the beginning of each attack. The resulting characterization enriches the body of knowledge with new insights, such as: (i) the Achilles’ Heel phenomenon is only valid for independent attacks, but not for dependent attacks; (ii) powerful attack strategies (e.g., targeted attacks and dependent attacks, dependent attacks and adaptive attacks) are not compatible and cannot help the attacker when used collectively. Our results shed some light on the design of robust complex networks.

[1]  Brent Byunghoon Kang,et al.  Peer-to-Peer Botnets: Overview and Case Study , 2007, HotBots.

[2]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[3]  Sakshi Pahwa,et al.  Abruptness of Cascade Failures in Power Grids , 2014, Scientific Reports.

[4]  Cohen,et al.  Resilience of the internet to random breakdowns , 2000, Physical review letters.

[5]  Amir Bashan,et al.  Percolation in networks composed of connectivity and dependency links , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[7]  D S Callaway,et al.  Network robustness and fragility: percolation on random graphs. , 2000, Physical review letters.

[8]  Albert-László Barabási,et al.  Emergence of scaling in complex networks , 2005 .

[9]  H. Stanley,et al.  Robustness of a partially interdependent network formed of clustered networks. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Shouhuai Xu,et al.  Exploiting Trust-Based Social Networks for Distributed Protection of Sensitive Data , 2011, IEEE Transactions on Information Forensics and Security.

[11]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[12]  Jean-Loup Guillaume,et al.  Impact of random failures and attacks on Poisson and power-law random networks , 2009, ACM Comput. Surv..

[13]  Harry Eugene Stanley,et al.  Robustness of Network of Networks with Interdependent and Interconnected links , 2013, ArXiv.

[14]  H. Stanley,et al.  Robustness of network of networks under targeted attack. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Harry Eugene Stanley,et al.  Robustness of a Network of Networks , 2010, Physical review letters.

[16]  P. Oscar Boykin,et al.  Disaster management in power-law networks: Recovery from and protection against intentional attacks , 2007 .

[17]  Guido Caldarelli,et al.  Cascades in interdependent flow networks , 2015, ArXiv.

[18]  S. Redner,et al.  Introduction To Percolation Theory , 2018 .

[19]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[20]  Shouhuai Xu,et al.  A Framework for Understanding Botnets , 2009, 2009 International Conference on Availability, Reliability and Security.

[21]  Yilun Shang Impact of self-healing capability on network robustness. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  H. Stanley,et al.  Networks formed from interdependent networks , 2011, Nature Physics.

[23]  Yilun Shang,et al.  Localized recovery of complex networks against failure , 2016, Scientific Reports.

[24]  P. Hines,et al.  Do topological models provide good information about electricity infrastructure vulnerability? , 2010, Chaos.

[25]  Yilun Shang,et al.  Effect of link oriented self-healing on resilience of networks , 2016 .

[26]  Alessandro Vespignani,et al.  Complex networks: The fragility of interdependency , 2010, Nature.

[27]  S. Havlin,et al.  Breakdown of the internet under intentional attack. , 2000, Physical review letters.

[28]  M. Newman,et al.  Random graphs with arbitrary degree distributions and their applications. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  S. Havlin,et al.  Optimization of network robustness to waves of targeted and random attacks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Shouhuai Xu,et al.  A new approach to modeling and analyzing security of networked systems , 2014, HotSoS '14.

[31]  Shouhuai Xu,et al.  Social Network-Based Botnet Command-and-Control: Emerging Threats and Countermeasures , 2010, ACNS.

[32]  Shouhuai Xu,et al.  A First Step towards Characterizing Stealthy Botnets , 2009, 2009 International Conference on Availability, Reliability and Security.

[33]  Harry Eugene Stanley,et al.  Robustness of interdependent networks under targeted attack , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Harry Eugene Stanley,et al.  Robustness of onion-like correlated networks against targeted attacks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Harry Eugene Stanley,et al.  Assortativity Decreases the Robustness of Interdependent Networks , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  Paul G. Spirakis,et al.  On the robustness of interconnections in random graphs: a symbolic approach , 2002, Theor. Comput. Sci..

[38]  Adilson E Motter,et al.  Network observability transitions. , 2012, Physical review letters.

[39]  Shouhuai Xu,et al.  L-hop percolation on networks with arbitrary degree distributions and its applications. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Nasir D. Memon,et al.  Friends of an enemy: identifying local members of peer-to-peer botnets using mutual contacts , 2010, ACSAC '10.

[41]  S. Havlin,et al.  Interdependent networks: reducing the coupling strength leads to a change from a first to second order percolation transition. , 2010, Physical review letters.

[42]  Sergey N. Dorogovtsev,et al.  Critical phenomena in complex networks , 2007, ArXiv.

[43]  Harry Eugene Stanley,et al.  Percolation of localized attack on complex networks , 2014, ArXiv.

[44]  Douglas Cochran,et al.  Conjoining Speeds up Information Diffusion in Overlaying Social-Physical Networks , 2011, IEEE Journal on Selected Areas in Communications.

[45]  David M. Pennock,et al.  Static and dynamic analysis of the Internet's susceptibility to faults and attacks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[46]  Hans J. Herrmann,et al.  Mitigation of malicious attacks on networks , 2011, Proceedings of the National Academy of Sciences.

[47]  Kousuke Yakubo,et al.  Structural robustness of scale-free networks against overload failures. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.