Parametric Equation for Capturing Dynamics of Cyber Attack Malware Transmission with Mitigation on Computer Network

Full Text (PDF, 500KB), PP.37-51

Views: 0 Downloads: 0

Author(s)

Falaye Adeyinka A 1,* Etuk Stella Oluyemi 2 Adama Ndako Victor 1 Ugwuoke Cosmas Uchenna 1 Olujimi Ogedengbe 3 Seun Ale 4

1. Department of Computer Science, Federal University of Technology Minna , Niger state, 920211, Nigeria.

2. Department of Information and media Technology, Federal University of Technology Minna, Niger state, 920211, Nigeria.

3. Integration and support mobile technologies, amuwo odofin industrial estate apapa-oshodi expresss way, Lagos.

4. Department of Mathematic Science, Federal University of Technology Minna, Niger state ,920211 Nigeria.

* Corresponding author.

DOI: https://doi.org/10.5815/ijmsc.2017.04.04

Received: 21 Dec. 2016 / Revised: 30 Dec. 2016 / Accepted: 5 Jan. 2017 / Published: 8 Nov. 2017

Index Terms

Homotopy Pertubation Method, SIR model, Malicious ware, Stability, Equillibrium

Abstract

One distress of network and data security professionals and advisers globally is about the abilities of infectious malicious agents (Malware) to invade the entire network terminals to wreak havoc extending from identity theft, financial fraud to systemic digital assault on critical national resources. This work studies the behavioural dynamics of the susceptible, infected, the recovered terminals on the mobile wireless network and the effective use of antivirus security signature as countermeasure. Solving for stability state, we found out that its Eigen value gives a positive value which means that the stability is at an unstable state. Using Homotopy perturbation to calculate the approximate solution of the system. The expression derived was simulated using a mathematical tool (mat lab).

Cite This Paper

Falaye Adeyinka A, Etuk Stella Oluyemi, Adama Ndako Victor, Ugwuoke Cosmas Uchenna, Olujimi Ogedengbe, Seun Ale,"Parametric Equation for Capturing Dynamics of Cyber Attack Malware Transmission with Mitigation on Computer Network", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.3, No.4, pp.37-51, 2017.DOI: 10.5815/ijmsc.2017.04.04

Reference

[1]Badakhshan B and Ariter D, (2007.) Simulation Based Analysis of Spreading Dynamics of Malware in Wireless Sensor Networks," International Conference on Sensor Technologies and Applications, 2007.Sensor Comm 2007. Publication Date: 14-20 Oct. 2007, page(s): 164 – 169.

[2]Davis N, Abbott L, Park J & James (2006): Epidemiology, Bio-mathematical Modeling, Demographic Analysis, Network Stealth Worms, Network Security. Blacksburg, Vol. 2; No 1, 149-156.

[3]Daley D and Gani J. Epidemic modelling: an introduction. Cambridge Univ Pr, 2001.

[4]Debany W. H, Modeling the spread of internet worms via persistently unpatched hosts," IEEE Network, Volume 22, Issue 2, March-April 2008 Page(s): 26-32.

[5]Falaye A, Osho O, Emehian M, Ale S. Dynamics of SCADA System Malware: Impacts on Smart Grid Electricity Networks and Countermeasures. International Conference on Information and Communication Technology and Application. ICTA 1st ed. Nigeria: 2016.

[6]Falaye A, Osho O, Emehian M, Ale S. Dynamics of SCADA System Malware: Impacts on Smart Grid Electricity Networks and Countermeasures. International Conference on Information and Communication Technology and Application. ICTA 1st ed. Nigeria: 2016.

[7]Garetto M, (1995) “Modeling Malware Spreading Dynamics,” extended version, http://www1.tlc.polito.it/˜garetto/pub/ virusreport.ps.gz

[8]Juil C. Martin 1, Legand L. Burge, Washington(2005) Modeling the Spread of Mobile Malware Department of Systems & Computer Science, Howard University2300 Sixth St. NW, Washington, DC, 20059 Department of Physics and Astronomy, Howard University 2355 Sixth St. NW, Washington, DC, 20059.

[9]Karyotis V. and Papavassiliou S., “Risk-based attack strategies for mobile ad hoc networks under probabilistic attack modeling framework,” Computer Networks, vol. 51, no. 9, pp. 2397–2410, 2007.

[10]Khouzani M., Altman E., and Sarkar S., “Optimal Quarantining of Wireless Malware Through Power Control,” in Proceedings of the Fourth Symposium on Information Theory and Applications, University of California at San Diego, 2009.

[11]Khouzani M., Sarkar S., and Altman E., “Maximum Damage Malware Attack in Mobile Wireless Networks,” To appear in Infocom 2010.

[12]Nikola v. & Ljupco K (2009): Modeling Malware Propagation in Networks Macedonian Academy of Sciences and Arts, Skopje, Macedonia. University of California San Diego, La Jolla, CA, USA.

[13]O'Donnell J, \When Malware Attacks (Anything but Win-dows)," IEEE Security & Privacy, Volume 6, Issue 3, May-June 2008, Page(s):68-70.

[14]Yan x and Y. Zou, “Optimal Internet Worm Treatment Strategy Based on the Two-Factor Model,” ETRI JOURNAL, vol. 30, no. 1, p. 81, 2008.

[15]Zhu Z., Cao G., Zhu S., Ranjan S., and Nucci A. “A social network based patching scheme for worm containment in cellular networks,” IEEE INFOCOM.

[16]Zou C C., Gong W., Towsley D., (2002) “Code Red Worm Propagation Modeling and Analysis,” 9th ACM Conference on Computer and Communications Security.

[17]C. Zou, W. Gong, and D. Towsley, “Worm propagation modeling and analysis under dynamic quarantine defense,”in Proceedings of the 2003 ACM workshop on Rapid Malcode, pp. 51–60, ACM New York, NY, USA, 2003.