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

Abstract

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.