An Integrated Dynamic Behavior Analysis and Adaptive Detection Framework for Enhanced Cloud Malware Detection

As malware threats continue to evolve and pose significant risks to cloud computing environments, there is a pressing need for advanced detection systems that can effectively address these challenges. This research introduces the Dynamic Behavior Analysis and Adaptive Detection Network (DBA-ADN), a novel framework designed to enhance malware detection through a combination of dynamic behavior analysis and adaptive detection techniques. DBA-ADN integrates real-time behavioral data with sophisticated adaptive algorithms to achieve superior detection performance. The study involves a comprehensive comparison of DBA-ADN against several benchmark systems, including MDCD, GloVe with CNN, CloudIntellMal, Hybrid Deep Learning Models, and DenseNet-121. The results reveal that DBA-ADN outperforms these established methods, achieving an impressive accuracy of 98.20% and an F1 Score of 0.99. This enhanced performance is attributed to DBA-ADN's ability to accurately identify both known and novel malware threats while minimizing false positives and false negatives. The framework's innovative approach addresses key limitations observed in existing solutions, as difficulties in handling sophisticated malware and inefficiencies in traditional detection techniques. By leveraging dynamic behavior analysis and adaptive algorithms, DBA-ADN not only improves detection accuracy but also reduces the complexity of deployment compared to its counterparts. The findings highlight DBA-ADN's potential to set a new benchmark in malware detection within cloud environments, offering a robust solution to the ongoing challenges of cybersecurity. This research contributes significantly to the field by providing a highly effective and adaptable approach to malware detection, laying the groundwork for future advancements and continued innovation in the domain.