ACE: A Static Android Malware Detection Method Based on Supervised Contrastive Learning

Smart and mobile devices are essential components of the Internet of Things (IoT) ecosystems, facilitating connectivity and automation across various domains. Due to its flexibility, the Android operating system is widely adopted in these devices. However, their increasing integration into IoT networks has introduced significant security risks, particularly from Android malware. To address these challenges, effective detection methods are needed to enhance IoT security. Given the success of contrastive learning in computer vision, researchers have increasingly explored its potential for Android malware detection. This article presents a static Android malware detection method that integrates deep learning with supervised contrastive learning. Based on the characteristic that contrastive learning enhances the model’s ability to effectively represent input samples, we design a novel contrastive loss based on structural similarity metrics and integrate it with contractive loss and binary cross-entropy loss to construct a hierarchical loss function for guiding model optimization. Furthermore, the method directly analyzes the classes.dex file from Android application package, eliminating the need for feature engineering or domain expertise, thus enhancing its applicability. Experimental results demonstrate that the proposed method achieves an 87.13% F1-score on the AndroZoo dataset, outperforming baseline models while maintaining computational efficiency and practical usability. Ablation studies validate the effectiveness of the hierarchical loss function in improving model performance and ensuring consistent malware representation within the same family.