Title :
A Virus Detection System Based on Artificial Immune System
Author :
Chao, Rui ; Tan, Ying
Author_Institution :
Key Lab. of Machine Perception, Peking Univ., Beijing, China
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.
Keywords :
artificial immune systems; computer viruses; radial basis function networks; support vector machines; RBF networks; SVM; affinity vectors; artificial immune system; autoimmunity detector elimination; clonal selection; detector set; hamming-max distance; k-nearest neighbor classiifier; negative selection; radial basis function neural network; shift r bit-continuous distance; support vector machine; virus detection system; Artificial immune systems; Change detection algorithms; Computational intelligence; Computational modeling; Computer security; Data mining; Data security; Detectors; Intrusion detection; Testing; AIS; SVM; clonal selection; negative selection;
Conference_Titel :
Computational Intelligence and Security, 2009. CIS '09. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-5411-2
DOI :
10.1109/CIS.2009.106
