Short review on metamorphic malware detection in Hidden Markov Models

Metamorphic malware is well known for evading signature-based detection. To cope up with numerous malware which can emerge easily by using open source malware generator, efficient detection in terms of accuracy and runtime performance shall be considered during analysis. Detection strategies such as...

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Main Authors: Yeong, T. Ling, Mohd Sani, Nor Fazlida
Format: Article
Language:English
Published: Advanced Research International Publication House 2017
Online Access:http://psasir.upm.edu.my/id/eprint/63209/
http://psasir.upm.edu.my/id/eprint/63209/1/Short%20review%20on%20metamorphic%20malware%20detection%20in%20Hidden%20Markov%20Models.pdf
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author Yeong, T. Ling
Mohd Sani, Nor Fazlida
author_facet Yeong, T. Ling
Mohd Sani, Nor Fazlida
author_sort Yeong, T. Ling
building UPM Institutional Repository
collection Online Access
description Metamorphic malware is well known for evading signature-based detection. To cope up with numerous malware which can emerge easily by using open source malware generator, efficient detection in terms of accuracy and runtime performance shall be considered during analysis. Detection strategies such as data mining combine with machine learning have been used by researchers for heuristically detecting malware. In this paper, we present Hidden Markov Model as an efficient metamorphic malware detection tool by exploring the common obfuscation techniques used in malware while reviewing and comparing the different studies that adopt HMM as a detection tool.
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spelling upm-632092018-08-20T06:25:48Z http://psasir.upm.edu.my/id/eprint/63209/ Short review on metamorphic malware detection in Hidden Markov Models Yeong, T. Ling Mohd Sani, Nor Fazlida Metamorphic malware is well known for evading signature-based detection. To cope up with numerous malware which can emerge easily by using open source malware generator, efficient detection in terms of accuracy and runtime performance shall be considered during analysis. Detection strategies such as data mining combine with machine learning have been used by researchers for heuristically detecting malware. In this paper, we present Hidden Markov Model as an efficient metamorphic malware detection tool by exploring the common obfuscation techniques used in malware while reviewing and comparing the different studies that adopt HMM as a detection tool. Advanced Research International Publication House 2017-02 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/63209/1/Short%20review%20on%20metamorphic%20malware%20detection%20in%20Hidden%20Markov%20Models.pdf Yeong, T. Ling and Mohd Sani, Nor Fazlida (2017) Short review on metamorphic malware detection in Hidden Markov Models. International Journal of Advanced Research in Computer Science and Software Engineering, 7 (2). pp. 62-69. ISSN 2277-6451; ESSN: 2277-128X http://www.ijarcsse.com/index.php/ijarcsse 10.23956/ijarcsse/V7I2/01218
spellingShingle Yeong, T. Ling
Mohd Sani, Nor Fazlida
Short review on metamorphic malware detection in Hidden Markov Models
title Short review on metamorphic malware detection in Hidden Markov Models
title_full Short review on metamorphic malware detection in Hidden Markov Models
title_fullStr Short review on metamorphic malware detection in Hidden Markov Models
title_full_unstemmed Short review on metamorphic malware detection in Hidden Markov Models
title_short Short review on metamorphic malware detection in Hidden Markov Models
title_sort short review on metamorphic malware detection in hidden markov models
url http://psasir.upm.edu.my/id/eprint/63209/
http://psasir.upm.edu.my/id/eprint/63209/
http://psasir.upm.edu.my/id/eprint/63209/
http://psasir.upm.edu.my/id/eprint/63209/1/Short%20review%20on%20metamorphic%20malware%20detection%20in%20Hidden%20Markov%20Models.pdf