AI-Driven Intelligent Intrusion Detection for Real-Time Network Threat Analysis in Enterprise and Cloud Networks

The rapid advancement of enterprise and cloud networks has significantly increased the complexity and scale of cyber threats, making conventional signature-based Intrusion Detection Systems (IDSs) insufficient. This paper presents the concept of an Intelligent Hybrid-Inference Network Intrusion Detection System (IHI-NIDS) powered by AI for real-time threat detection in high-velocity enterprise and cloud environments. IHI-NIDS combines Gated Recurrent Units (GRUs) for temporal sequence modelling with a multi-head self-attention mechanism, accounting for both short-range packet dynamics and long-range dependencies that signal multi-stage attacks. The system supports hybrid datasets that combine enterprise packet flows (PCAP/NetFlow) and cloud telemetry (VPC Flow Logs). It uses strong scaling and dimensionality reduction via PCA as the main approach to guarantee computational efficiency. Assessment of a composite dataset of 1.2 million flows indicates a detection rate of 97.8, recall of 96.1, F1-score of 96.5, and false alarm rate of 2.1, validating the capability to detect both enterprise-level and cloud-based threats. SHAP-based explainability highlights key features that affect predictions, which security analysts can use. Based on latency measurements, it can be deployed in real time to high-throughput networks, whereas temporal generalisation tests demonstrate its ability to withstand unobserved attack sequences. This article makes IHI-NIDS a scalable, interpretable, high-performance solution for current hybrid network security, filling the gap between operational viability and predictive AI-based defence.