NDLI: A Virus Detection System Based on Artificial Immune System

Content Provider	IEEE Xplore Digital Library
Author	Rui Chao Ying Tan
Copyright Year	2009
Abstract	A virus detection system (VDS) based on artificial immune system (AIS) is proposed in this paper. VDS at first generates the detector set from virus files in the dataset, negative selection and clonal selection are applied to the detector set to eliminate autoimmunity detectors and increase the diversity of the detector set in the non-self space respectively. Two novel hybrid distances called hamming-max and shift r bit-continuous distance are proposed to calculate the affinity vectors of each file using the detector set. The affinity vectors of the training set and the testing set are used to train and test classifiers respectively. VDS compares the detection rates using three classifiers, k-nearest neighbor (KNN), RBF networks and SVM when the length of detectors is 32-bit and 64-bit. The experimental results show that the proposed VDS has a strong detection ability and good generalization performance.
Starting Page	6
Ending Page	10
File Size	353164
Page Count	5
File Format	PDF
ISBN	9781424454112
e-ISBN	9781424454129
DOI	10.1109/CIS.2009.106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Change detection algorithms Computational modeling Data security negative selection SVM AIS Data mining Artificial immune systems Intrusion detection Detectors Computer security clonal selection Computational intelligence Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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