A Quantum Immune Temporal Bayesian Framework for Ultra High Accuracy AI Driven Intrusion Detection and Adaptive Mitigation in Elastic Cloud Computing Environments

Interactive resource implementation, multi-tenancy, and massive data transfer present an ever more complicated risk to cloud computing environments. Conventional intrusion detection systems are not effective in detecting new and sophisticated attacks within this environment. This paper attempts to solve this issue by suggesting an artificial intelligence-powered intrusion detection and mitigation system that is specially implemented in cloud environments. The methodology incorporates 4 complicated and rarely applied algorithms: Hierarchical Temporal Memory, Bayesian Attack Graph Inference, Artificial Immune Systems with Negative Selection and Clonal Expansion, and Quantum-inspired Evolutionary Algorithms, to ensure improved anomaly detection, attack prediction and adaptive response. The framework has a layered way of working, which allows the creation of ongoing learning and real-time mitigation. As per experimental evidence, it has also been revealed that the framework will be characterized by much better detects, fewer false alarms and faster mitigation than traditional AI-based methods, which opens the possibility of enhancing cloud web security resilience.