Hybrid Deep Learning and Behavioral Analysis for Enhanced Malware Detection in Banking

As malware threats targeting the banking sector continue to evolve, traditional detection methods are becoming less effective, often leading to high false positive rates and missed emerging threats. This research introduces an innovative hybrid malware detection framework that integrates advanced deep learning algorithms with behavioral analysis, specifically designed to handle the complexities of modern banking systems. Unlike conventional approaches, this framework employs a novel deep learning architecture to detect subtle and evolving malware patterns, while its behavioural analysis component provides real-time insights into unusual user and system activities. This dual-layered approach significantly improves detection accuracy and reduces false positives, addressing critical shortcomings in existing detection methods. A key innovation of this research is the use of a carefully curated, domain-specific dataset that includes a wide range of real-world malware samples and legitimate banking transactions. The dataset features adaptive and previously unseen malware strains, making the framework highly resilient against zero-day attacks. Rigorous performance evaluations, conducted through stress tests in simulated banking environments, demonstrate that the framework outperforms current models in detection rates, response times, and operational efficiency. This breakthrough solution not only enhances the detection of evolving malware but also minimizes operational disruptions, marking a substantial advancement in securing critical banking infrastructure against sophisticated cyber threats.