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All Journal IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Theophilus Wellem
Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana
Computer Science & IT Control & Systems Engineering

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Klasifikasi serangan malware berbasis citra menggunakan algoritma YOLOv11 Daniel Darren Richardo; Theophilus Wellem
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): IT-Explore Juni 2026
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v5i2.2026.pp232-240

Abstract

Malware represents an evolving cybersecurity threat that demands more effective detection methods. Conventional signature-based detection systems have limitations in identifying new variants, driving the development of deep learning-based approaches. This research implements and evaluates four variants of the YOLOv11 algorithm (n, s, m, l) for malware classification based on visual image representation. The dataset consists of 22,056 malware and benign images, divided into 70% training, 15% validation, and 15% testing across 8 classes (adware, backdoor, benign, downloader, spyware, trojan, virus, worm). Each model was trained for 100 epochs with batch size 32 using Google Colab with GPU support. Results demonstrate that all variants achieve high accuracy (97.8%-98.1%) with YOLOv11m as the best performer (98.1%). YOLOv11n offers optimal balance between accuracy (97.9%) and efficiency (1.5M parameters, 0.3 ms/img inference) ideal for real-time applications. This research surpasses previous methods such as K-NN (97.18%) and hybrid CNN (96.55%) with superior inference speed (0.3-0.9 ms/img vs tens to hundreds of ms/img), proving the effectiveness of YOLOv11 for fast, accurate, and scalable malware detection.
Co-Authors Daniel Darren Richardo