This paper presents an introduction to a useful predicament of effects to our on-line world i.e. Cyber assaults and safety. In starting of the paper we describe the objects used for cyber assaults and inform the method of spreading them akin to secondary reminiscence, e mail attachments, instantaneous messages or malicious bots. After this paper describe the roll of mathematical modeling and simulation to unravel the predicament with an tremendous mathematical overview.An analysis of the most important variety has been made. We derive global steadiness of a worm-free state. Additionally, initial simulation outcome show off the optimistic influence of increasing security measures on worm propagation in various group.Efficiency of antivirus program and crashing of the nodes accordingly of worms attack is seriously analyzed.Numerical method is employed to get to the bottom of the procedure of equations developed and interpretation of the yields wonderful revelations Cyber safety structure and viable factors of cyber look after model are moreover studied for locating the research gaps. On the final this paper finds some gaps and possible tactics to bridge these gaps.
Virus, Worms, Differential Equation, Illustration Messaging, FTP, E-Mail
 Saini D.K. (2011). A mathematical model for the effect of malicious object on computer network immune system, Applied Mathematical Modeling, Vol. 35, pp. 3777-3787, DOI: 10.1016/.2011.02.025
 Mishra B.K., Saini D.K. (2007). Mathematical models on computer viruses, Elsevier International Journal of Applied Mathematics and Computation, Vol. 187, No. 2, pp. 929-936.
 Saini D.K., Saini H. (2008). VAIN: a stochastic model for dynamics of malicious objects, the ICFAI Journal of Systems Management, Vol. 6, No. 1, pp. 14- 28.
 Saini H., Saini D.K. (2007). Malicious object dynamics in the presence of Anti Malicious Software, European Journal of Scientific Research, Vol. 18, No. 3, pp. 491-499.
 Fixed Coefficients Block Backward Differentiation Formulas for the Numerical Solution of Stiff Ordinary Differe ntial Equations Ibrahim. pp. 508-520. ISSN 1450-216X.
 Chen T., Jamil N. (2006). Effectiveness of quarantine in worm epidemics, IEEE International Conference on Communications, pp. 2142-2147.
 Keeling M.J., Eames K.T.D. (2005). Network and epidemic models, J. Roy. Soc. Interf., Vol. 2, No. 4, pp. 295 – 307.
 An epidemiological model of virus spread and Cleanup, Matthew M. Williamson,HP Labs Bristol,
 Filton Road, Stoke Gifford, BS34 8QZ, UK Newman M.E.J., Forrest S., Balthrop J. (2002). Email networks and the spread of computer virus, Phys. Rev. E, Vol. 66, pp. 035101-1-035101-4.
 Draief M., Ganesh A., Massouili L. (2008). Thresholds for virus spread on network, Ann. Appl. Prob., Vol. 18, No.2, pp. 359 – 369.
 Li G., Zhen J. (2004). Global stability of an SEI epidemic model with general contact rate, Chaos Solitons and Fractals, Vol. 23, pp. 997–1004.
 Stability theory for ordinary differential equations, J.P LaSalle. Author links open the author workspace.
Center for Dynamical Systems, Brown University.
 Krieger, Basel, (1980) 12] J. O. Kephart, A. (1995). Biologically inspired immune system for computers, Proceedings of International Joint Conference on Artificial Intelligence, pp. 137-145.
 Kephart J.O., White S.R. (1993). Measuring, and modeling computer virus prevalence, IEEE Computer Security Symposium on Research in Security, and Privacy, pp. 2-15.
 Kephart J.O., White S.R., Chess D.M. (1993). Computers, and epidemiology, IEEE Spectrum, Vol. 30, No. 5, pp. 20-26.
 Kermack W.O., McKendrick A.G. (1927). Contributions of mathematical theory to epidemics, I, Proceedings of the Royal Society of London, Series A, Vol. 115, pp. 700-721.