Adaptive Deep Learning Architectures for Enhanced Cloud-based Intrusion Detection and Behavioral User Authentication

In today's increasingly interconnected world, securing cloud-based systems against sophisticated cyber threats has become paramount.The growing reliance on cloud infrastructure demands advanced techniques to protect sensitive data and maintain the integrity of network operations.Traditional Intrusion Detection Systems (IDS) and authentication mechanisms often fall short in addressing the dynamic and complex nature of modern cyber-attacks.The proposed Hybrid Ensemble Deep Learning Model (HEDLM) introduces a novel multi-branch deep learning framework that enhances security by integrating adaptive feature extraction, dynamic model fusion, and real-time classification strategies.Unlike traditional CNN-LSTM models, HEDLM employs a hybrid architecture combining CNN, LSTM, GRU, and Transformer networks, allowing the model to extract spatial-temporal features dynamically while adapting to evolving intrusion patterns.Additionally, an adaptive ensemble learning strategy is integrated, which optimally selects the best-performing deep learning model based on the attack category.A reinforcement learningdriven self-optimization mechanism further fine-tunes the model to minimize false positives and improve generalization.To ensure real-time performance in cloud environments, the model is deployed using a distributed processing framework, enhancing detection efficiency and scalability.Extensive evaluations on UNSW-NB15, CICIDS2017, and KDD99 datasets demonstrate that HEDLM achieves 99.7% accuracy, 99.8% precision, and 99.6% recall, significantly outperforming traditional IDS and deep learning models.This multi-perspective adaptive learning approach establishes a highly resilient security framework capable of mitigating sophisticated cyber-attacks in cloud infrastructures.