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Investigation of agent-based simulation of malicious software

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EN
Abstrakty
EN
Epidemics of malicious software are actual problem and network worms are one of the most important issues. Identifying trends in network worm distribution, finding the factors that influence the spread of the Internet worm will help to identify the effective preventive and precautionary measures to prevent epidemics of malicious software. To solve the problem of the development of advanced security mechanisms against network worms, different approaches to modeling the spreading of worms have been studied. Deterministic models of propagation of computer viruses in a heterogeneous network, taking into account its topological and architectural features have been analyzed and improved. Agent-based model of network worm propagation have been developed. Simulated model is based on epidemic approach to modeling. SAIDR structure of agent-based model has been used for simulation of malicious software of “network worm” type. A comparative study of developed mathematical models has been conducted. Comparative graphs of the dependence of the infected nodes number on the time of the computer system functioning in the propagation of the epidemic have been built. Research carried out by the example of the Code Red worm propagation.
Twórcy
  • National Aerospace University “Kharkiv Aviation Institute”
autor
  • National Aerospace University “Kharkiv Aviation Institute”
Bibliografia
  • 1. Nachenberg C., 2000. The Evolving Virus Threat 23rd NISSC Proceedings.
  • 2. Weaver N., 2001. Warhol Worms: The Potential for Very Fast Internet Plagues.
  • 3. Chernyshev Y., Chumachenko T., Chumachenko D., Tovstik A., 2012. System of Simulation of Epidemic Diseases Spreading. Proceedings of East West Fuzzy Colloquium 2012 (19th Zittau Fuzzy Colloquium, September 5–7, 2012), 154–161.
  • 4. Kephart J., Chess D., White S., 1993. Computers and Epidemiology. IEEE Spectrum 30 (5). 20–26.
  • 5. Kephart J., White S., 1993. Measuring and Modeling Computer Virus Prevalence. Proceedings of the IEEE Symposium on Security and Privacy. 2–15.
  • 6. Wang C., Knight J., ElderM., 2000. On Viral Propagation and the Effect of Immunization. Proceedings of 16th ACM Annual Computer Applications Conference. 246–256.
  • 7. Staniford S., Paxson V., Weaver N., 2002. How to Own the Internet in Your Spare Time. 11-th Usenix Security Symposium. 149–167.
  • 8. Moore D., Shannon C., 2003. The Spread of the Code-Red Worm. Center for Applied Internet Data Analysis.
  • 9. Baroyan O., Rvachev L., 1978. Forecasting of influenza epidemics in USSR. Medicine #2.131-137. (in Russian).
  • 10. Wooldridge M., 2009. An Introduction To Multiagent Systems. 468.
  • 11. Chumachenko T., Chumachenko D., Sokolov O., 2013. Multiagent simulation of the hepatitis B epidemic process. Online journal of public health informatics, 5 (1).
  • 12. Boyko N., Kutyuk O., 2016. Basic concepts of evolution in agents calculating and agents system. ECONTECHMOD. An International Quarterly Journal, Vol. 05, No. 2. 69-76.
  • 13. Chernyshev Y., Chumachenko D., Tovstik A., 2013. Development of intelligent agents for simulation of hepatitis B epidemic process. Proceedings of East West Fuzzy Colloquium 2013 (20th Zittau Fuzzy Colloquium, September 25–27, 2013). 161–168.
  • 14. Bobalo Y., Politanskyi R., Klymash M., 2015. Traffic simulation in a telecommunication system based on queuing systems with different input flows. ECONTECHMOD. An International Quarterly Journal, Vol. 04, No. 1. 11-16.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b8dba1a9-df9e-42cb-8c94-c272aef6116a
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