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An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

Om Prakash Samantray, Satya Narayan Tripathy
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 13
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781799859895|DOI: 10.4018/IJISP.2021100102
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MLA

Samantray, Om Prakash, and Satya Narayan Tripathy. "An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms." IJISP vol.15, no.4 2021: pp.18-30. http://doi.org/10.4018/IJISP.2021100102

APA

Samantray, O. P. & Tripathy, S. N. (2021). An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms. International Journal of Information Security and Privacy (IJISP), 15(4), 18-30. http://doi.org/10.4018/IJISP.2021100102

Chicago

Samantray, Om Prakash, and Satya Narayan Tripathy. "An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms," International Journal of Information Security and Privacy (IJISP) 15, no.4: 18-30. http://doi.org/10.4018/IJISP.2021100102

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Abstract

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

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