A Hybrid Deep Learning Approach for Adaptive Cloud Threat Detection with Integrated CNNs and RNNs in Cloud Access Security Brokers
DOI:
https://doi.org/10.18196/jrc.v6i3.25618Keywords:
Behavior-Based Anomaly Detection, Adaptive CASB, CNNs, RNNs, Cloud Security, Real-Time MitigationAbstract
Cloud computing offers on-demand, scalable, and cost-effective deployment models but also struggles with sophisticated and rapidly-evolving cybersecurity threats. Static, rule-based approaches to data moved by traditional Cloud Access Security Brokers (CASBs) are seldom able to detect these threats. In this work, we introduce Adaptive CASB a new framework built on a new hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs learn spatial features in network traffic and RNNs find temporal dependencies, leading to robust static and dynamic threat detection. The system combines behavior-based anomaly detection with real-time threat intelligence applied to the Internet, providing adaptability to new attacks such as zero-day attacks. Experiments on benchmark datasets (e.g. NSL-KDD, UNSW-NB15) prove that our model outperforms the others with accuracy of 95%, precision of 92% and recall of 94%, which is significantly better than CASBs based on traditional techniques and machine learning models. Moreover, the automated threat response capabilities of the system send alerts and implement containment measures that mitigate threats in real-time. Such an Adaptive CASB framework signifies a scalable and cost-effective response to contemporary cloud security challenges, whilst also paving the way for future advancements, such as XAI integration and edge-computing optimization.
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