Stochastic modeling of computer virus spreading with warning signals

Abstract Modeling and understanding virus spreading is a crucial issue in computer security. Epidemiological models have been proposed to deal with this problem. We investigate the dynamics of computer virus spreading by considering an stochastic susceptible-infected-removed-susceptible (SIRS) model where immune computers send warning signals to reduce the propagation of the virus among the rest of the computers in the network. We perform an exhaustive analysis of the main indicators of the spread and persistence of the infection. To this end, we provide a detailed study of the quasi-stationary distribution, the number of cases of infection, the extinction time and the hazard time.

[1]  Sihan Qing,et al.  A survey and trends on Internet worms , 2005, Comput. Secur..

[2]  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.

[3]  M. Keeling,et al.  On methods for studying stochastic disease dynamics , 2008, Journal of The Royal Society Interface.

[4]  Denis Mollison,et al.  The Analysis of Infectious Disease Data. , 1989 .

[5]  Jesus R. Artalejo,et al.  Modeling computer virus with the BSDE approach , 2013, Comput. Networks.

[6]  Simon Cauchemez,et al.  Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London , 2008, Journal of The Royal Society Interface.

[7]  James C. Frauenthal,et al.  Stochastic Epidemic Models , 1980 .

[8]  Chuang Lin,et al.  Performance analysis of email systems under three types of attacks , 2010, Perform. Evaluation.

[9]  A. Gómez-Corral,et al.  Extinction times and size of the surviving species in a two-species competition process , 2012, Journal of mathematical biology.

[10]  Matt J. Keeling,et al.  Networks and the Epidemiology of Infectious Disease , 2010, Interdisciplinary perspectives on infectious diseases.

[11]  Erik A. van Doorn,et al.  Quasi-stationary distributions , 2011 .

[12]  Philip K. Pollett,et al.  Survival in a quasi-death process , 2008 .

[13]  Donald F. Towsley,et al.  Code red worm propagation modeling and analysis , 2002, CCS '02.

[14]  Fred Brauer,et al.  An Introduction to Networks in Epidemic Modeling , 2008, Mathematical Epidemiology.

[15]  David J. Marchette,et al.  Modeling computer virus prevalence with a susceptible-infected-susceptible model with reintroduction , 2004, Comput. Stat. Data Anal..

[16]  Suleyman Kondakci,et al.  Internet epidemiology: healthy, susceptible, infected, quarantined, and recovered , 2011, Secur. Commun. Networks.

[17]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[18]  H. Tijms A First Course in Stochastic Models , 2003 .

[19]  M. J. Lopez-Herrero,et al.  Quasi-stationary and ratio of expectations distributions: a comparative study. , 2010, Journal of theoretical biology.

[20]  J V Ross,et al.  On parameter estimation in population models. , 2006, Theoretical population biology.

[21]  J R Artalejo,et al.  On the number of recovered individuals in the SIS and SIR stochastic epidemic models. , 2010, Mathematical biosciences.

[22]  J. R. Artalejo,et al.  The SIS and SIR stochastic epidemic models: a maximum entropy approach. , 2011, Theoretical population biology.

[23]  Tadashi Dohi,et al.  Markovian modeling and analysis of Internet worm propagation , 2005, 16th IEEE International Symposium on Software Reliability Engineering (ISSRE'05).

[24]  P. O’Neill,et al.  Bayesian estimation of the basic reproduction number in stochastic epidemic models , 2008 .

[25]  Zhonghua Zhang,et al.  Global results for a cholera model with imperfect vaccination , 2012, J. Frankl. Inst..

[26]  G. Gibson,et al.  Novel moment closure approximations in stochastic epidemics , 2005, Bulletin of mathematical biology.

[27]  Lixia Zhang,et al.  A taxonomy of biologically inspired research in computer networking , 2010, Comput. Networks.

[28]  Minaya Villasana,et al.  An extension of the Kermack-McKendrick model for AIDS epidemic , 2005, J. Frankl. Inst..

[29]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[30]  Meryl E. Wastney,et al.  Publishing, interpreting, and accessing models , 1998 .

[31]  P K Pollett,et al.  On parameter estimation in population models II: multi-dimensional processes and transient dynamics. , 2009, Theoretical population biology.

[32]  H. Andersson,et al.  Stochastic Epidemic Models and Their Statistical Analysis , 2000 .

[33]  Gunter Bolch,et al.  Queueing Networks and Markov Chains - Modeling and Performance Evaluation with Computer Science Applications, Second Edition , 1998 .

[34]  Jesús R. Artalejo,et al.  A state‐dependent Markov‐modulated mechanism for generating events and stochastic models , 2009 .

[35]  H. zu Dohna,et al.  Fitting parameters of stochastic birth-death models to metapopulation data. , 2010, Theoretical population biology.

[36]  William Hugh Murray,et al.  The application of epidemiology to computer viruses , 1988, Comput. Secur..

[37]  Philip D O'Neill,et al.  A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods. , 2002, Mathematical biosciences.

[38]  Jeffrey O. Kephart,et al.  Measuring and modeling computer virus prevalence , 1993, Proceedings 1993 IEEE Computer Society Symposium on Research in Security and Privacy.

[39]  T. Britton,et al.  Statistical studies of infectious disease incidence , 1999 .

[40]  Fernando Vega-Redondo,et al.  Complex Social Networks: Searching in Social Networks , 2007 .

[41]  E. Seneta,et al.  On quasi-stationary distributions in absorbing continuous-time finite Markov chains , 1967, Journal of Applied Probability.

[42]  M G Roberts,et al.  The pluses and minuses of 0 , 2007, Journal of The Royal Society Interface.

[43]  A. M. Cohen Numerical Methods for Laplace Transform Inversion , 2007 .

[44]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  Tom Britton,et al.  Stochastic epidemic models: a survey. , 2009, Mathematical biosciences.

[46]  Tadashi Dohi,et al.  Estimating Computer Virus Propagation Based on Markovian Arrival Processes , 2010, 2010 IEEE 16th Pacific Rim International Symposium on Dependable Computing.

[47]  J R Artalejo,et al.  Stochastic epidemic models revisited: analysis of some continuous performance measures , 2012, Journal of biological dynamics.

[48]  Ronald Meester,et al.  Modeling and real-time prediction of classical swine fever epidemics. , 2002, Biometrics.

[49]  L. Allen An Introduction to Stochastic Epidemic Models , 2008 .

[50]  Jing Li,et al.  The Failure of R 0 , 2011, Computational and mathematical methods in medicine.