ARTIFICIAL IMMUNE SYSTEMS APPROACH FOR MALWARE DETECTION: NEURAL NETWORKS APPLYING FOR IMMUNE DETECTORS CONSTRUCTION

Authors

  • Sergei Bezobrazov
  • Vladimir Golovko

DOI:

https://doi.org/10.47839/ijc.7.2.509

Keywords:

Artificial immune system, computer security system, malicious code detection, LVQ neural network

Abstract

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.

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Published

2014-08-01

How to Cite

Bezobrazov, S., & Golovko, V. (2014). ARTIFICIAL IMMUNE SYSTEMS APPROACH FOR MALWARE DETECTION: NEURAL NETWORKS APPLYING FOR IMMUNE DETECTORS CONSTRUCTION. International Journal of Computing, 7(2), 44-50. https://doi.org/10.47839/ijc.7.2.509

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Section

Articles