Malware Avoidance Using Two Epidemic Layers

Authors

  • R. Siddarth  Dhanalakshmi College of Engineering, Chennai, Tamilnadu, India
  • T. M. Sounderarajan Mothilal  Dhanalakshmi College of Engineering, Chennai, Tamilnadu, India
  • P. Sathish Saravanan  Dhanalakshmi College of Engineering, Chennai, Tamilnadu, India

Keywords:

Malware, Malware Propagation, Two Layers, Power Law, Supervised Classification

Abstract

Malware are malicious software programs deployed by cyber attackers to compromise computer. The solution to this problem is desperately desired by cyber defenders as the network security community does not yet have solid answers. The main scope of our project to investigate how malware propagate in networks from a global perspective. We propose a two layer malware propagation model to describe the development of a given malware at the Internet level. Compared with the existing single layer epidemic models, the proposed model represents malware propagation better in large-scale networks. We propose a two layer malware propagation model to describe the development of a given malware at the Internet level. Compared with the existing single layer epidemic models, the proposed model represents malware propagation better in large-scale networks

References

[1] B. Stone-Gross, M. Cova, L. Cavallaro, B. Gilbert, M. Szydlowski, R. Kemmerer, C. Kruegel, and G. Vigna, “Your botnet is my botnet: Analysis of a botnet takeover,” in CCS ’09: Proceedings of the 2009 ACM conference on computer communication security, 2009.

[2] D. Dagon, C. Zou, andW. Lee, “Modeling botnet propagation using time zones,” in Proceedings of the 13 th Network and Distributed System Security Symposium NDSS, 2006.

[3] M. A. Rajab, J. Zarfoss, F. Monrose, and A. Terzis, “My botnet is bigger than yours (maybe, better than yours): why size estimates remain challenging,” in Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets, 2007.

[4] D. Dagon, C. C. Zou, and W. Lee, “Modeling botnet propagation using time zones,” in NDSS, 2006.

[5] A. M. Jeffrey, xiaohua Xia, and I. K. Craig, “When to initiate hiv therapy: A control theoretic approach,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 11, pp. 1213–1220, 2003.

[6] R. Dantu, J.W. Cangussu, and S. Patwardhan, “Fast worm containment using feedback control,” IEEE Transactions on Dependable and Secure Computing, vol. 4, no. 2, pp. 119–136, 2007.

[7] S. H. Sellke, N. B. Shroff, and S. Bagchi, “Modeling and automated containment of worms,” IEEE Trans. Dependable Sec. Comput., vol. 5, no. 2, pp. 71–86, 2008.

[8] P. De, Y. Liu, and S. K. Das, “An epidemic theoretic framework for vulnerability analysis of broadcast protocols in wireless sensor networks,” IEEE Trans. Mob. Comput., vol. 8, no. 3, pp. 413–425, 2009.

[9] G. Yan and S. Eidenbenz, “Modeling propagation dynamics of bluetooth worms (extended version),” IEEE Trans. Mob. Comput., vol. 8, no. 3, pp. 353–368, 2009.

[10] C. C. Zou, W. Gong, D. Towsley, and L. Gao, “The monitoring and early detection of internet worms,” IEEE/ACM Trans. Netw., vol. 13, no. 5, pp. 961–974, 2005. 

Downloads

Published

2015-04-25

Issue

Section

Research Articles

How to Cite

[1]
R. Siddarth, T. M. Sounderarajan Mothilal, P. Sathish Saravanan, " Malware Avoidance Using Two Epidemic Layers, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 2, pp.183-185, March-April-2015.