Performance evaluation of various deep convolutional neural network models through classification of malware
by Zareen Tasneem; Maria Afnan; Md. Arman Hossain; Md. Mahbubur Rahman; Samrat Kumar Dey
International Journal of Information and Computer Security (IJICS), Vol. 21, No. 3/4, 2023

Abstract: Malware, a collective name for malicious programs, is a piece of software, system or scripts, causing damage to the system. Lately, use of internet has favoured criminal activities like malware assaults. Hence, malware classification comes in the first line of defense. Machine learning (ML) techniques have drawn attention to malware classifiers over all other techniques in the last decade. Very little investigation highlights the results of the existing studies in malware classification using ML approach. The progress is slow due to difficulties of developing a deep learning system: dataset collection, labelling, feature extraction, model construction, training and testing the models, and evaluation. A systematic way of summarising the current knowledge also lacks in latest methods. This study utilises a systematic literature review and presents implementation of different CNN models for malware classification into their respective families. Its objective is to analyse the most popular architectures and evaluate their results.

Online publication date: Wed, 09-Aug-2023

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