Innovative Approaches for Enhanced Security in Cloud Network Traffic: An Adaptive Deep Learning Framework

The rapid growth of cloud computing has introduced significant security challenges, particularly in protecting network traffic from advanced cyber threats. This research presents an adaptive deep learning framework to enhance cloud network security by detecting and mitigating threats in real-time. By leveraging multi-modal data sources, such as network traffic logs, system performance metrics, and user behavior analytics, the framework identifies threats like Distributed Denial of Service (DDoS) attacks, malware, and unauthorized access. A comprehensive dataset, including network traffic data, system logs, and security alerts from various cloud environments, was collected. Pre-processing involved normalization, augmentation, and feature extraction tailored to multi-modal inputs. Convolutional Neural Networks (CNNs) analyzed network traffic features, while Random Forest and Gradient Boosting algorithms examined system and user behavior. The research also explores the impact of network conditions like bandwidth fluctuations and latency on model performance and proposes Internet of Things (IoT)-based monitoring for real-time data collection.