Open AccessARTIFICIAL IMMUNE SYSTEMS APPROACH FOR MALWARE DETECTION: NEURAL NETWORKS APPLYING FOR IMMUNE DETECTORS CONSTRUCTION
Author(s) -
Sergei Bezobrazov,
Vladimir Golovko
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.7.2.509
Subject(s) - learning vector quantization , computer science , malware , artificial immune system , artificial neural network , computer virus , artificial intelligence , detector , software , machine learning , code (set theory) , computer security , operating system , programming language , telecommunications , set (abstract data type)
This paper presents an approach for solving unknown computer viruses detection problem based on the Artificial Immune System (AIS) method, where immune detectors represented neural networks. The AIS is the biologically-inspired technique which have powerful information processing capabilities that makes it attractive for applying in computer security systems. Computer security systems based on AIS principles allow detect unknown malicious code. In this work we are describing model build on the AIS approach in which detectors represent the Learning Vector Quantization (LVQ) neural networks. Basic principles of the biological immune system (BIS) and comparative analysis of unknown computer viruses detection for different antivirus software and our model are presented.