DETEKSI DAN KLASIFIKASI MALWARE PADA CITRA GRAYSCALE DENGAN MENGGUNAKAN DEEP LEARNING

VICTORY, DANIEL SANDWI and Supardi, Julian and Arsalan, Osvari (2023) DETEKSI DAN KLASIFIKASI MALWARE PADA CITRA GRAYSCALE DENGAN MENGGUNAKAN DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

This research focuses on the utilization of Deep Learning techniques for malware detection and classification. By representing malware samples as grayscale images, a Deep Learning model based on Convolutional Neural Networks (CNN) is developed. The model is trained using a dataset containing grayscale malware samples. Experimental results demonstrate a high level of accuracy of the Deep Learning model in detecting and classifying malware. This research contributes to the advancement of computer security systems by effectively addressing the challenges posed by malware threats using the Deep Learning approach. The research results show that the evaluation of the testing results on the trained model architecture yielded an average Accuracy of 0.96, Precision of 0.97, Recall of 0.97, and F1-Score of 0.96 using 20% of the dataset, consisting of 474 malware images.

Item Type:	Thesis (Undergraduate)
Subjects:	T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Daniel Sandwi Victory
Date Deposited:	08 Aug 2023 07:23
Last Modified:	08 Aug 2023 07:23
URI:	http://repository.unsri.ac.id/id/eprint/126186

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