Android Malware Detection based on Feature-pair Bonding: A Hybrid Detection Model

Android has emerged as the dominant operating system over the past decade, enjoying widespread usage. However, its popularity has also made it an attractive target for malware proliferation. One reason behind this vulnerability is Android’s acceptance of app installations from unauthorized sources, which gives a window to malicious code writers to disseminate applications malicious in nature which in turn compromise the Android device. To tackle this issue, much research in the literature tried to develop malware analysis and detection systems that employ both static and dynamic analysis techniques. These approaches aim to identify and mitigate malware threats. However, existing research in this field still faces challenges in terms of efficiently and accurately detecting malware. Moreover, achieving accurate malware detection often requires significant resources, which can be a concern on resource-limited mobile devices. Despite advancements, the current state of research struggles to strike a balance between detection accuracy and resource utilization, particularly in the context of mobile devices with limited capabilities. In the current research paper, we present a new methodology for constructing a hybrid malware detection model that operates independently without relying on a pre-existing Machine Learning classifier. Our proposed model operates by leveraging the concept of feature-pairs, where pairs of features that exhibit strong correlations are identified. By calculating the feature-pair scores, we determine whether an application is benign or malicious based on these scores. This self-sufficient model eliminates the need for an external classifier, offering a unique approach to malware detection.