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http://dx.doi.org/10.3745/KIPSTB.2007.14-B.1.059

Fuzzy Cluster Based Diagnosis System for Classifying Computer Viruses  

Rhee, Hyun-Sook (동양공업전문대학 전산정보학부)
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.
Keywords
Fuzzy Cluster Analysis; Malicious Code; Knowledge Acquisition; Decision Status Measure;
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