MIDALF—multimodal image and audio late fusion for malware detection
Setia Juli Irzal Ismail, Hendrawan Hendrawan, Budi Rahardjo, Tutun Juhana, Yasuo Musashi
Abstract
Malware detection remains a critical challenge in cybersecurity due to the rapid evolution of sophisticated malware and adversarial threats. Traditional detection systems struggle to adapt to dynamic malware behavior and are particularly vulnerable to adversarial attacks, where subtle modifications evade detection mechanisms. To address these limitations, MIDALF—a novel multimodal image and audio late fusion approach for malware detection is proposed. Unlike previous works that rely on single-modal or static features, MIDALF transforms malware binaries into image and audio representations. Self-supervised learning is employed to extract robust features from image representations, while convolutional neural networks analyze audio spectrograms. To integrate these diverse modalities, five late fusion techniques were explored, identifying logistic regression as the most effective. The proposed approach achieves an accuracy of 99.7% in classifying malware and benign samples on the BODMAS dataset, significantly outperforming baseline methods (p < 0.0001, Wilcoxon signed-rank test). Furthermore, MIDALF demonstrates strong robustness against adversarial malware generated using generative adversarial networks (GANs), maintaining a detection accuracy of 95.1% under adversarial conditions. These findings highlight MIDALF’s ability to address limitations in existing works by leveraging multimodal representations, integrating complementary data features, and enhancing resilience against adversarial attacks. This study offers a novel pathway towards more effective and robust malware detection systems.