Provable Security of Networks

We propose a definition of {\it security} and a definition of {\it robustness} of networks against the cascading failure models of deliberate attacks and random errors respectively, and investigate the principles of the security and robustness of networks. We propose a {\it security model} such that networks constructed by the model are provably secure against any attacks of small sizes under the cascading failure models, and simultaneously follow a power law, and have the small world property with a navigating algorithm of time complex $O(\log n)$. It is shown that for any network $G$ constructed from the security model, $G$ satisfies some remarkable topological properties, including: (i) the {\it small community phenomenon}, that is, $G$ is rich in communities of the form $X$ of size poly logarithmic in $\log n$ with conductance bounded by $O(\frac{1}{|X|^{\beta}})$ for some constant $\beta$, (ii) small diameter property, with diameter $O(\log n)$ allowing a navigation by a $O(\log n)$ time algorithm to find a path for arbitrarily given two nodes, and (iii) power law distribution, and satisfies some probabilistic and combinatorial principles, including the {\it degree priority theorem}, and {\it infection-inclusion theorem}. By using these principles, we show that a network $G$ constructed from the security model is secure for any attacks of small scales under both the uniform threshold and random threshold cascading failure models. Our security theorems show that networks constructed from the security model are provably secure against any attacks of small sizes, for which natural selections of {\it homophyly, randomness} and {\it preferential attachment} are the underlying mechanisms.

[1]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[2]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.

[3]  B. Bollobás The evolution of random graphs , 1984 .

[4]  F. Chung,et al.  Complex Graphs and Networks , 2006 .

[5]  Boris G. Pittel,et al.  Note on the Heights of Random Recursive Trees and Random m-ary Search Trees , 1994, Random Struct. Algorithms.

[6]  H. Chernoff A Note on an Inequality Involving the Normal Distribution , 1981 .

[7]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[8]  P. Kaye Infectious diseases of humans: Dynamics and control , 1993 .

[9]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[10]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[11]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[12]  Jacob Goldenberg,et al.  Using Complex Systems Analysis to Advance Marketing Theory Development , 2001 .

[13]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[14]  Amin Saberi,et al.  On certain connectivity properties of the Internet topology , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..

[15]  E. Young Contagion , 2015, New Scientist.

[16]  Béla Bollobás,et al.  The Diameter of a Scale-Free Random Graph , 2004, Comb..

[17]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[18]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[19]  Éva Tardos,et al.  Which Networks are Least Susceptible to Cascading Failures? , 2011, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.

[20]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[21]  Mark S. Granovetter Threshold Models of Collective Behavior , 1978, American Journal of Sociology.