Lightweight Malware Classification with FORTUNATE: Precision Meets Computational Efficiency

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Abstract
After detecting a malicious artifact, classifying malware into specific families becomes an essential step to understand the threat's behavior, implement mitigation strategies, and develop proactive defenses. This task is particularly challenging due to the diversity of malware formats, the rapid evolution of obfuscation and packing techniques, as well as the scarcity of labeled data for training robust models. Additionally, the high volume of samples generated daily demands solutions that combine high accuracy and computational efficiency. Although transformer-based models are widely recognized as the state-of-the-art for sequence processing tasks, their high computational demands limit their practical application in resource-constrained environments. In this work, we present FORTUNATE, a lightweight framework that leverages LSTM networks with one-hot encoding to classify malware based on variable-length opcode sequences. The framework adopts an optimized opcode extraction process focused on reducing redundancies and representing data in compact vectors, minimizing computational costs. Experimental results indicate that FORTUNATE achieves accuracies of 99.82% for active malware and 99.81% for inactive malware, with an average classification time of only 56 ms per sample, significantly outperforming related works. The obtained results demonstrate that lightweight artificial intelligence approaches can deliver competitive performance in malware classification, especially in scenarios with computational constraints. FORTUNATE not only fills an important gap in malware classification but also establishes a foundation for future research aimed at optimizing the balance between accuracy, efficiency, and scalability.

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