TDBAMLA: Temporal and dynamic behavior analysis in Android malware using LSTM and attention mechanisms

Abstract

The increasing ubiquity of Android devices has precipitated a concomitant surge in sophisticated malware attacks, posing critical challenges to cybersecurity infrastructures worldwide. Existing models have achieved significant strides in malware detection but often suffer from high false-positive rates, lower recall, and computational delays, thus demanding a more efficient and accurate system. Current techniques primarily rely on static features and simplistic learning models, leading to inadequate handling of temporal aspects and dynamic behaviors exhibited by advanced malware. These limitations compromise the detection of modern, evasive malware, and impede real-time analysis. This paper introduces a novel framework for Android malware detection that incorporates Temporal and Dynamic Behavior Analysis using Long Short-Term Memory (LSTM) networks and Attention Mechanisms. We further propose development of an efficient Grey Wolf Optimized (GWO) Decision Trees to find the most salient API call patterns associated with malwares. An Iterative Fuzzy Logic (IFL) layer is also deployed before classification to assess the "trustworthiness" of app metadata samples. For Ongoing Learning, we propose use of Deep Q-Networks (DQNs), which helps the reinforcement learning model to adapt more quickly to changes in the threat landscapes. By focusing on crucial system calls and behavioral characteristics in real-time, our model captures the nuanced temporal patterns often exhibited by advanced malwares. Empirical evaluations demonstrate remarkable improvements across multiple performance metrics. Compared to existing models, our approach enhances the precision of malware identification by 8.5 %, accuracy by 5.5 %, and recall by 4.9 %, while also achieving an 8.3 % improvement in the Area Under the Receiver Operating Characteristic Curve (AUC), with higher specificity and a 4.5 % reduction in identification delay. In malware pre-emption tasks, our model outperforms by improving precision by 4.3 %, accuracy by 3.9 %, recall by 4.9 %, AUC by 3.5 %, and increasing specificity by 2.9 %. These gains make our framework highly applicable for real-time detection systems, cloud-based security solutions, and threat intelligence services, thereby contributing to a safer Android ecosystem.