Fuzzy Cluster Based Diagnosis System for Classifying Computer Viruses 


Vol. 14,  No. 1, pp. 59-64, Feb.  2007
10.3745/KIPSTB.2007.14.1.59


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  Abstract

In these days, malicious codes have become reality and evolved significantly to become one of the greatest threats to the modern society where important information is stored, processed, and accessed through the internet and the computers. Computer virus is a common type of malicious codes. The standard techniques in anti-virus industry is still based on signatures matching. The detection mechanism searches for a signature pattern that identifies a particular virus or stain of viruses. Though more accurate in detecting known viruses, the technique falls short for detecting new or unknown viruses for which no identifying patterns present. To cope with this problem, anti-virus software has to incorporate the learning mechanism and heuristic. In this paper, we propose a fuzzy diagnosis system(FDS) using fuzzy c-means algorithm(FCM) for the cluster analysis and a decision status measure for giving a diagnosis. We compare proposed system FDS to three well known classifiers-KNN, RF, SVM. Experimental results show that the proposed approach can detect unknown viruses effectively.

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[IEEE Style]

H. S. Rhee, "Fuzzy Cluster Based Diagnosis System for Classifying Computer Viruses," The KIPS Transactions:PartB , vol. 14, no. 1, pp. 59-64, 2007. DOI: 10.3745/KIPSTB.2007.14.1.59.

[ACM Style]

Hyun Sook Rhee. 2007. Fuzzy Cluster Based Diagnosis System for Classifying Computer Viruses. The KIPS Transactions:PartB , 14, 1, (2007), 59-64. DOI: 10.3745/KIPSTB.2007.14.1.59.

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