IJEEDC Malware Classification System using Deep Learning Technique

Journal Paper

Paper Title :Malware Classification System using Deep Learning Technique

Author :Shivarti Naik, Amita Dessai

Article Citation :Shivarti Naik ,Amita Dessai , (2022 ) " Malware Classification System using Deep Learning Technique " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 7-12, Volume-10,Issue-9

Abstract : Abstract - There has been a massive increase in the variety and volume of malware in recent years. This poses a serious security threat to major organisations and individuals. In order to combat the rapid growth of malware, it is essential to identify malware samples. This helps to effectively analyze large numbers of malware variants. The customary machine learning approaches often require a significant amount of resources in feature engineering. Recently, advanced machine learning such as deep learning have outperformed the conventional machine learning algorithms. Convolutional Neural Network (CNN), a deep learning approach, is found to be remarkable in image classification. In this paper, we employ Convolutional Neural Network algorithm to classify malware samples. We evaluated this technique on Malimg malware dataset and accomplished an accuracy of 95.73%. Keywords - Malware classification, Obfuscation, Cyber security, Deep learning, Convolutional Neural Network.

Type : Research paper

Published : Volume-10,Issue-9


	\|		PDF	\|	Viewed - 43	\|	Published on 2022-12-21

Jan. 2025
Submitted Papers	:	80
Accepted Papers	:	10
Rejected Papers	:	70
Acc. Perc	:	12%
Issue Published	:	141
Paper Published	:	1771
No. of Authors	:	4924

Published : Volume-10,Issue-9

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