IMAGE-BASED DEEP LEARNING METHOD FOR EFFECTIVE MALWARE DETECTION

Y. Imamverdiyev, E. Baghirov, I.J. Chukwu

About the authors

Y. Imamverdiyev, E. Baghirov, I.J. Chukwu

Yadigar Imamverdiyev – Associate professor, Azerbaijan Technical University, H. Javid, 25, Baku, Azerbaijan. Area of scientific interest: Information security, biometric technologies, e-government security, artificial intelligence in security, cryptographic systems, social network analysis, distributed computing, mathematical logic.

Elshan Baghirov – PhD Candidate, Institute of Information Technology, Ministry of Science and Education of the Republic of Azerbaijan, B. Vahabzade, 9A, Baku, Azerbaijan. Area of scientific interest: machine learning, cybersecurity, malware detection.

Ikechukwu John Chukwu – Researcher, Kadir Has University, Istanbul, Türkiye, and Ss. Cyril and Methodius University in Skopje (UKIM), North Macedonia, Area of scientific interest: deep learning, image-based analysis, software security. The article was submitted to the editorial office on 30.11.2024

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UDC: 519.71
DOI: 10.24412/2413-7383- 106-122
Language: Russian

Abstract: В статье рассматривается метод обнаружения вредоносного программного обеспечения на основе анализа изображений, представленных в оттенках серого. Для анализа используются 13 современных сверточных нейронных сетей, включая DenseNet201, MobileNet, и другие, на основе набора данных Malimg. Проведены эксперименты, включающие обучение и настройку гиперпараметров для оптимизации производительности моделей. Показано, что модели, такие как DenseNet201 и MobileNet, достигают высокой точности, полноты, точности и F1-метрики. Данный подход позволяет улучшить процесс обнаружения вредоносного ПО, обеспечивая высокую эффективность и устойчивость к традиционным методам обхода систем защиты. Область применения работы — современные системы кибербезопасности, включая разработку новых методов анализа вредоносного ПО и защиту от кибератак.

Keywords: Malware detection, malware analysis, transfer learning, image-based detection, cybersecurity

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