Trustworthy AI for Network Intrusion Detection: A Critical Review of Deep Learning, Federated Learning, Explainability, and Large Language Models
Keywords:
Cybersecurity, Explainable AI, Federated learning, Network intrusion detection, Retrieval-augmented generation, Trustworthy AIAbstract
A network intrusion detector is valuable only when its alerts help defenders make sound decisions. This critical narrative review compares supervised and anomaly-based deep learning, federated detection, explainable AI, and large-language-model (LLM) assistance for network intrusion detection (NIDS). Its principal contribution is a study-level comparison of existing methods and a unified, deployment-oriented assessment of generalization, privacy, adversarial resilience, explanation quality, and analyst workload. The literature synthesis identifies three recurring findings: (i) high scores on static intrusion datasets do not establish cross-site or temporal reliability; (ii) federated data locality does not itself prevent gradient leakage, malicious updates, or unequal performance across clients; and (iii) plausible explanations and LLM-generated incident summaries require evidence tracing, security controls, and human verification. The study recommends chronological and cross-organizational evaluation, operational alert metrics, calibrated thresholds, threat-model-driven testing, and staged deployment validation. The article does not present a newly trained detector or a systematic meta-analysis; its conclusions are qualitative, evidence-qualified recommendations for more trustworthy cybersecurity operations.
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