A Hierarchical Artificial Immune Model for Virus Detection

Abstract-- As viruses become more complex, existing antivirus methods are inefficient to detect various forms of viruses, especially new variants and unknown viruses. Inspired by immune system, a hierarchical artificial immune system (AIS) model, which is based on matching in three layers, is proposed to detect a variety of forms of viruses. In the bottom layer, a non-stochastic but guided candidate virus gene library is generated by statistical information of viral key codes. Then a detecting virus gene library is upgraded from the candidate virus gene library using negative selection. In the middle layer, a novel storage method is used to keep a potential relevance between different signatures on the individual level, by which the mutual cooperative information of each instruction in a virus program can be collected. In the top layer, an overall matching process can reduce the information loss considerably. Experimental results indicate that the proposed model can recognize obfuscate...

• Type: Conference
• Year: 2009
• Where: CIS
• Authors: Wei Wang, Pengtao Zhang, Ying Tan, Xingui He
• Keywords: Applied Computing, Candidate Virus Gene, CIS 2009, Unknown Viruses, Virus Gene Library