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General Information
    • ISSN: 1793-8236 (Online)
    • Abbreviated Title Int. J. Eng. Technol.
    • Frequency:  Quarterly 
    • DOI: 10.7763/IJET
    • APC: 500 USD
    • Managing Editor: Ms. Jennifer Zeng
    • Abstracting/ Indexing: Inspec (IET), CNKI Google Scholar, EBSCO, ProQuest, Crossref, Ulrich Periodicals Directory, Chemical Abstracts Services (CAS), etc.
    • E-mail: ijet_Editor@126.com
Editor-in-chief
IJET 2013 Vol.5(2): 315-319 ISSN: 1793-8236
DOI: 10.7763/IJET.2013.V5.566

Malware Detection using Computational Biology Tools

Ali Alatabbi, Moudhi Al-Jamea, and Costas S. Iliopoulos

Abstract—The Internet is considered to be as a rich platform of information where many people get benefit from its access but still they are being attacked by computer malwares and various other threats which distract their normal work flow to be carried out in an efficient manner. In this paper, we give an overview of the efficient read aligner software termed as REAL which is used for next generation sequencing. It reads structures as a tool to detect computer Malware. Using this tools a dynamic computer malware detection model has been presented in this paper that can detect the malwares to prevent attacks which might cause damaging or stealing sensitive information. This model is inspired by REAL which is an efficient read aligner for next generation sequencing for processing biological data. New anti-Malware technologies are introduced to the world by the clock, but at the same time new malware techniques have also emerged to misuse these technologies. Experimental results of this study shows that the proposed system is efficient and it is a novel way for detecting malware code embedded in different types of computer files, using bioinformatics tools with consistency and accuracy in detecting the malware and it was able to complete the assignment in high speed without excessive memory usages.

Index Terms—Malware detection, pattern recognition, pattern matching, security.

The authors are with the Department of Informatics, King’s College London, London WC2R 2LS, United Kingdom (e-mail: ali.alatabbi@kcl.ac.uk, mudhi.aljamea@kcl.ac.uk, csi@kcl.ac.uk).

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Cite: Ali Alatabbi, Moudhi Al-Jamea, and Costas S. Iliopoulos, "Malware Detection using Computational Biology Tools," International Journal of Engineering and Technology vol. 5, no. 2, pp. 315-319, 2013.

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