A state feedback impulse model for computer worm control

Computer worm is a worldwide threat to the safety of Internet, which caused billions of dollars in damages over the past decade. Software patches have been widely used as one of approaches to protect computers against computer worms. In this study, an impulsive state feedback model was employed to study the transmission of computer worm and the preventive effect of operating system patching. The existence of order-1 periodic solution and its stability were proved with a novel method. The results demonstrated that the application of software patches is an effective approach to constrain the deluge of computer worm. Numerical simulation results were presented to support the theoretical analysis.

[1]  Lansun Chen,et al.  Periodic Solution of Prey-Predator Model with Beddington-DeAngelis Functional Response and Impulsive State Feedback Control , 2012, J. Appl. Math..

[2]  Toshikazu Kuniya,et al.  On global stability of a nonresident computer virus model , 2014 .

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

[4]  Toshikazu Kuniya,et al.  Global stability of nonresident computer virus models , 2015 .

[5]  Cynthia S. Hood,et al.  Analysis of a scanning model of worm propagation , 2008, Journal in Computer Virology.

[6]  Guoping Pang,et al.  Periodic solution of the system with impulsive state feedback control , 2014 .

[7]  José Roberto Castilho Piqueira,et al.  A modified epidemiological model for computer viruses , 2009, Appl. Math. Comput..

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

[9]  Chen Lan-sun.  Pest Control and Geometric Theory of Semi-Continuous Dynamical System , 2011 .

[10]  Bernhard Plattner,et al.  Host behaviour based early detection of worm outbreaks in Internet backbones , 2005, 14th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprise (WETICE'05).

[11]  Donald F. Towsley,et al.  On the performance of Internet worm scanning strategies , 2006, Perform. Evaluation.

[12]  Lansun Chen,et al.  Homoclinic bifurcation of prey-predator model with impulsive state feedback control , 2014, Appl. Math. Comput..

[13]  Chunjin Wei,et al.  HETEROCLINIC BIFURCATIONS OF A PREY–PREDATOR FISHERY MODEL WITH IMPULSIVE HARVESTING , 2013 .

[14]  Lu-Xing Yang,et al.  The pulse treatment of computer viruses: a modeling study , 2014 .

[15]  Ahmed Elaiw,et al.  Global properties of a class of virus infection models with multitarget cells , 2012 .

[16]  Bing Liu,et al.  DYNAMICS ON A HOLLING II PREDATOR–PREY MODEL WITH STATE-DEPENDENT IMPULSIVE CONTROL , 2012 .

[17]  David Moore,et al.  Internet quarantine: requirements for containing self-propagating code , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[18]  Yuan Tian,et al.  Geometric approach to the stability analysis of the periodic solution in a semi-continuous dynamic system , 2014 .

[19]  Lu-Chuan Ceng,et al.  Hybrid Method with Perturbation for Lipschitzian Pseudocontractions , 2012, J. Appl. Math..

[20]  Jeremy T. Bradley,et al.  Observing Internet Worm and Virus Attacks with a Small Network Telescope , 2006, PASM@FM.

[21]  Huaixing Li,et al.  Global stability of a delayed SIRS computer virus propagation model , 2014, Int. J. Comput. Math..

[22]  A. Elaiw,et al.  Global properties of a cell mediated immunity in HIV infection model with two classes of target cells and distributed delays , 2014 .

[23]  Pele Li,et al.  A survey of internet worm detection and containment , 2008, IEEE Communications Surveys & Tutorials.

[24]  Jun Li,et al.  Investigating the Impact of Real-World Factors on Internet Worm Propagation , 2007, ICISS.