Volume 12, Number 2, December 2016 - DOI: http://dx.doi.org/10.21700/ijcis.2016.274

IJCIS

Computing and Information Sciences is a peer reviewed journal that is committed to timely publication of original research, surveying and tutorial contributions on the analysis and development of computing and information science. The journal is designed mainly to serve researchers and developers, dealing with information and computing. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. The journal is published 2-3 times per year with distribution to librarians, universities, research centers, researchers in computing, mathematics, and information science. The journal maintains strict refereeing procedures through its editorial policies in order to publish papers of only the highest quality. The refereeing is done by anonymous Reviewers. Often, reviews take four months to six months to obtain, occasionally longer, and it takes an additional several months for the publication process.

DOI: http://dx.doi.org/10.21700/ijcis.2016.128

Virus Recognition Based on Combination of Hashing and Neural Networks

Mohamed H. Almeer email: almeer@qu.edu.qa

Department of Computer Science and Engineering, Qatar University, Doha, Qatar.

 

Received: 30 July 2016
Revised: 12 August 2016
Accepted: 10 September 2016
Published: 25 December 2016

Abstract: In this paper, we propose an intelligent first-warning system for virus code detection based on Artificial Neural Networks (ANNs). The proposed system operates in accordance with the basic principles of ANNs to conduct pattern matching of 32-bit hash signatures and detect virus signatures by means of the hashing applied to the byte content of executable code. The proposed system can accurately detect virus code in accordance with information it has learned, and gives false positive ratios within acceptable ranges. The results of experiments conducted show that the combination of 32-bit hashing and neural networks results in a low false positive rate. This paper also discusses the key ideas and approaches, along with the necessary adaptations and adjustments undertaken in the neural network model underlying the proposed early warning virus detection system.

Keywords: Hashing, Hash Code; BKDR Hash Function; ANN; Neural Networks; Virus detection.


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