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.
E. Egho-Promise, Ekereuke Udoh, Edita Gashi et al.· Information· 0 citations
Large language models used for clinical diagnostic reasoning are sensitive to sociolinguistic register, not just clinical content. We term this failure mode Narrative Anchoring: identical clinical facts expressed in different registers cause diagnostic outputs to diverge. Unlike prior demographic-bias work, which manipulates explicit identity tokens such as race or income, our benchmark isolates register as the sole channel of variation, with no demographic marker present in any form. We construct a dataset of 1,000 USMLE clinical vignettes, each rewritten into three sociolinguistically distinct personas under an independently audited fact-preservation guarantee, verified by a separate model that never sees the generation prompt. Across seven language models spanning three architecture families and scales, Narrative Anchoring is statistically significant under direct prompting in every model tested, with a Narrative Anchoring Gap of 0.064 to 0.151. Chain-of-thought reasoning and explicit debiasing instructions reduce the bias only partially, and their apparent gains are frequently confounded by accuracy collapse. We introduce NarrativeShield, a three-agent pipeline that structurally extracts and verifies clinical facts before diagnostic reasoning begins, reducing the Narrative Anchoring Gap to near-zero ($-0.004$ to $0.037$) and achieving the lowest rate of severely unstable decisions (DSS $<$ 0.8) of any method across all models, at a modest and mechanistically expected accuracy cost for most models. A stress test using a non-instruction-tuned base model shows that executing a debiasing intervention at all is gated by zero-shot instruction-following ability, not prompt content alone. We release our dataset, human-validated for fact preservation, as a standalone resource for studying register-based clinical bias.
Prabhjot Singh, Pritam Deka, V. Chennareddy· 0 citations
