Klasifikasi Malware Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Website
DOI:
https://doi.org/10.54066/jptis.v2i2.1931Keywords:
Malware, Convolutional Neural Network (CNN), IoT23 dataset, web-based malware detection, deep learningAbstract
This study aims to develop a web-based malware detection system using Convolutional Neural Network (CNN) utilizing the IoT23 dataset. Malware is malicious software that can exploit security vulnerabilities in computer systems, steal data, and degrade performance. The implementation of this detection system involves CNN, capable of extracting important features from both visual and textual data, applied to malware classification. The IoT23 dataset comprises 23 scenarios of IoT network traffic, including traffic from malware-infected devices. The study results show that the developed web application can detect malware attacks with accuracy, precision, recall, and F1-score of 99% on separate data scenarios. This CNN-based detection system has proven effective in identifying and classifying malware attacks, contributing to the enhancement of network and device security.
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