What question did this study set out to answer?

The central aim is to develop a robust approach for classifying malware into various families using advanced techniques.

May 16, 2026Open Access

Towards Robust Malware Detection with a Multiclass Dataset for Intelligent Learning

Key Points

The central aim is to develop a robust approach for classifying malware into various families using advanced techniques.
Proposed a hybrid technique combining sandbox analysis with AI models.
Created a multiclass dataset (AU-PEMAL-2025) containing 10,839 records of 26 malware families.
Trained five machine learning and three deep learning models to validate detection accuracy.
Machine learning classifiers achieved accuracy rates of 0.9945, 0.9788, and 0.9485.
Deep learning models achieved accuracy rates of 0.9932, 0.9591, and 0.9286.
The approach effectively detected obfuscated malware variants, enhancing protection for organizations against unseen threats.

Abstract

Malware has evolved from the early Creeper virus into highly sophisticated and organized cyber threats. Over time, it grew in sophistication, adopting advanced techniques, stealth tactics, and autonomous propagation. Modern malware leverages encryption, obfuscation, zero-day exploits, and AI-assisted techniques to conduct stealthy and persistent attacks. Classification of its exact family is the end goal to defend and mitigate the latest attacks. Researchers have contributed significantly and introduced many techniques to tackle malware threats. Binary detection is performed at a large scale, but very little in multi-class classification. In this research, a hybrid technique is proposed by combining a sandbox with AI models to extract hidden patterns and classify its category and family with high accuracy. A dataset (AU-PEMAL-2025) is prepared, which includes 10,839 records of 26 malware families. Five ML and three DL models are trained on the newly created dataset to validate its effectiveness. The ML classifiers achieved the highest accuracies of 0.9945, 0.9788, and 0.9485, while the DL models achieved 0.9932, 0.9591, and 0.9286 accuracies with minimal losses in detection and multi-class classification of category and family, respectively. Our findings reveal that the proposed approach can efficiently detect the obfuscated malware variants and safeguard organizations from unseen malware threats.

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