Selection of Next Generation Anti-Virus against Virus Attacks in Networks Using AHP

Full Text (PDF, 987KB), PP.29-35

Views: 0 Downloads: 0


Sounak Paul 1,* Bimal Kumar Mishra 2

1. Department of Information Technology, Birla Institute of Technology, Mesra, Ranchi, India

2. Department of Applied Mathematics, Birla Institute of Technology, Mesra, Ranchi, India

* Corresponding author.


Received: 20 May 2012 / Revised: 14 Sep. 2012 / Accepted: 2 Nov. 2012 / Published: 8 Feb. 2013

Index Terms

Virus, Antivirus, AHP, Network security, Weight, Defense index


Defending against virus attacks in network is a vital part of network security. With the rapid evolution of viruses, its defense mechanism has also been evolved over the years. But given the diversity and complexity of virus propagation and its attack behavior, no defense mechanism is equipped fully to protect the network from such attacks. Several antiviruses are available in the market. But none can give full proof solution to malicious attacks in communication networks. In this paper we present a mechanism to measure and compare the relative ability of antivirus against various kinds of viruses. We construct a hierarchical structure for different virus defense mechanism. Using Analytical Hierarchy Process (AHP) we construct a pair wise comparison matrix and find the value of corresponding Eigen vectors; we then apply the theory of AHP to calculate weight of each defense index. We validated our technique with an example. Our method can provide a strong reference to design an optimal network security solution.

Cite This Paper

Sounak Paul, Bimal Kumar Mishra, "Selection of Next Generation Anti-Virus against Virus Attacks in Networks Using AHP", International Journal of Computer Network and Information Security(IJCNIS), vol.5, no.2, pp.29-35, 2013. DOI:10.5815/ijcnis.2013.02.04


[1]Xiaoqi Jia, Xi Xiong, Jiwu Jing, and Peng Liu, Using Purpose Capturing Signatures to Defeat Computer Virus Mutating. In the proceeding of ISPEC, LNCS 6047, pp. 153–171, 2010.
[2], "A history of computer viruses", help/answers/the-history-of-computerviruses.shtml
[3]Venkatesan, Ashwini, Code Obfuscation and Virus Detection, Master's Projects. 2008, Paper 116.
[4]I.Muttik, Silicon Implants, Virus Bulletin, pp. 8-10, May 1997.
[5]Venkatachalam, Sujandharan, Detecting Undetectable Computer Viruses, Master's Projects, 2010 Paper 156.
[6]T.L. Saaty, Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, RWS Publications, U.S.A., 2000.
[7]Q. Y. Song and A. Jamalipour, A network selection mechanism for next generation networks, IEEE Int. Conf. Communication (ICC). Vol.2, pp.1418-1422, 2005.
[8]Sounak Paul, Sukumar Nandi, Indrajeet Singh, A Dynamic Balanced-Energy Sleep Scheduling Scheme in Heterogeneous Wireless Sensor Networks, In the proceeding of IEEE 16th Intl. Conf on Networks(ICON), 2008.
[9]Ming Liu, Lansheng Han, M.Zou, Qiwen Liu, An Evaluating Model for Anti-virus Ability Based on AHP. , in the proceeding of IEEE Intl Conf on Computational Science & Engineering, 2009.
[10]Jau-Hwang Wang, Peter S. Deng, Virus Detection Using Data Mining Techniques, in the proceeding of IEEE 37th Intl Conf on Security Technology, pp 71-76, 2003.
[11]Yu Zhang, Tao Li, Renchao Qin, A Dynamic Immunity-based Model for Computer Virus Detection, in the proceeding of IEEE International Symposiums on Information Processing, 2008.
[12]Venkatesan, Ashwini, Code Obfuscation and Virus Detection, Master's Projects 2008, Paper 116.
[13]Li Peng, Wang Ru-chuan , Zhang Wei, Key technologies of new malicious code developments and defensive measures in communication networks, The Journal of China Universities of Posts and Telecommunications, Elsevier, 2010.
[14]P.Szor, P.Ferrie, The art of computer virus research and defense, Addison-Wesley, 2005.
[15]Wing Wong, Analysis and detection of metamorphic viruses. Master's Thesis, 2006.
[16]G. Tesauro, J.O. Kephart, G.B. Sorkin, "Neural networks for computer virus recognition", IEEE Expert, vol. 11, no. 4, pp., 1996.
[17]P. Li, M. Salour, and X. Su, "A survey of internet worm detection and containment," Communications Surveys & Tutorials, IEEE, vol. 10, pp. 20-35, 2008.
[18]Symantec Anti-virus:
[19]Lanjia Wang, Zhichun Li, Yan Chen, Zhi Fu , Xing Li, Thwarting zero-day polymorphic worms with network-level length-based signature generation, IEEE/ACM Transaction on Networking, vol 18, no. 1, pp, 53-66, 2010.
[20]J. Newsome, B. Karp, and D. Song, Polygraph: Automatically generating signatures for polymorphic worms, in Proc. IEEE S&P, 2005, pp. 226–241.
[21]Z. Li, M. Sanghi, Y. Chen, M. Kao, and B. Chavez, Hamsa: Fast signature generation for zero-day polymorphic worms with provable attack resilience, in Proc. IEEE S&P, 2006, pp. 33–47.
[22]Y. Tang and S. Chen, Defending against Internet worms: A signature based approach, in Proc. IEEE INFOCOM, 2003, pp. 1384–1394.
[23]C. Kruegel et al., Polymorphic worm detection using structural information of executables, in Proc. RAID, 2005, pp. 207–226.
[24]J. Newsome and D. Song, Dynamic taint analysis for automatic detection, analysis, and signature generation of exploits on commodity software, presented at the NDSS, 2005.
[25]Z. Liang and R. Sekar, Fast and automated generation of attack signatures: A basis for building self-protecting servers, in Proc. ACM CCS, 2005, pp. 213–222.
[26]X. Wang et al., Packet vaccine: Black-box exploit detection and signature generation, in Proc. ACM CCS, 2006, pp. 37–46.