IMAGE-BASED DEEP LEARNING METHOD FOR EFFECTIVE MALWARE DETECTION

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Contacts

Yadigar Imamverdiyev
Associate professor,
Azerbaijan Technical University, H. Javid, 25, Baku, Azerbaijan.
Research interests: 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.
Research interests: 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.
Research interests: deep learning, image-based analysis, software security.

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UDC 519.71
DOI 10.24412/2413-7383-106-122
Language: English
Annotation: The article examines a method for malware detection based on the analysis of grayscale images. Thirteen advanced convolutional neural networks, including DenseNet201, MobileNet, and others, are utilized for analysis based on the Malimg dataset. Experiments were conducted, including training and hyperparameter tuning, to optimize the models' performance. It is shown that models such as DenseNet201 and MobileNet achieve high accuracy, precision, recall, and F1 scores. This approach enhances the malware detection process, ensuring high efficiency and resilience against traditional methods of bypassing security systems. The application area of this work includes modern cybersecurity systems, the development of new methods for malware analysis, and protection against cyberattacks.
Keywords: Malware detection, malware analysis, transfer learning, image-based detection, cybersecurity.

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Release: 1(36)'2025
Chapter: METHODS AND SYSTEMS OF INFORMATION PROTECTION, INFORMATION SECURITY
How to quote: Y. Imamverdiyev, E. Baghirov, I.J. Chukwu. IMAGE-BASED DEEP LEARNING METHOD FOR EFFECTIVE MALWARE DETECTION // Problems of artificial intelligence. 2025. №1.

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