{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:32:47Z","timestamp":1776940367513,"version":"3.51.4"},"reference-count":0,"publisher":"Agora University of Oradea","issue":"2","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["INT J COMPUT COMMUN, Int. J. Comput. Commun. Control"],"abstract":"<jats:p>Intrusion Detection Systems (IDS) are critical to ensuring cybersecurity in complex, dynamic, and data-intensive network environments. Traditional IDS, whether signature-based or classical machine learning (ML)-based, struggle to adapt to evolving attack patterns and to provide explainable decisions in real time. This paper presents a comprehensive evolutionary framework leading to a new unified model: the Neuro-Fuzzy Reinforcement Transformer Intrusion Detection System (NFRT-IDS). Three intermediate hybrid algorithms, a Transformer-CNN (Convolutional Neural Network) IDS, a Fuzzy\u2013Ensemble IDS, and a Deep Q-Learning based Artificial Neural Network (DQL\u2013ANN) IDS, are first proposed, rigorously optimized through cross-validation, and extensively evaluated on benchmark datasets (CICIDS2017, UNSW-NB15, and BoT-IoT). These models respectively address deep feature extraction, interpretability, and adaptive decision optimization challenges in IDS, while providing complementary architectural and learning advantages. Their integration inspired the unified NFRT-IDS framework, which combines global attentionbased feature learning, fuzzy inference for uncertainty modeling and rule-based explainability, and reinforcement learning (DQL agent) for dynamic parameter adaptation and performance-driven optimization. Experimental results demonstrate that NFRT-IDS achieves superior performance, reaching 99.98% accuracy and F1-score on CICIDS2017, with a 0.31% False Alarm Rate (FAR) and 0.999 AUC, outperforming state-of-the-art hybrid models. Beyond single-dataset evaluation, NFRT-IDS exhibits strong cross-dataset generalization, maintaining consistent accuracy and F1- scores when trained on CICIDS2017 and evaluated on heterogeneous datasets such as UNSW-NB15 and BoT-IoT. Furthermore, the framework ensures scalability, robustness, and interpretability, enabling efficient real-time intrusion detection in modern IoT and cloud environments.<\/jats:p>","DOI":"10.15837\/ijccc.2026.2.7405","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:45:50Z","timestamp":1773315950000},"source":"Crossref","is-referenced-by-count":1,"title":["NFRT\u2013IDS: A Unified Neuro-Fuzzy Reinforcement Transformer Architecture for Adaptive and Explainable Intrusion Detection"],"prefix":"10.15837","volume":"21","author":[{"given":"Ouail","family":"MJAHED","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soukaina","family":"MJAHED","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"6528","published-online":{"date-parts":[[2026,3,12]]},"container-title":["INTERNATIONAL JOURNAL OF COMPUTERS  COMMUNICATIONS &amp; CONTROL"],"original-title":[],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:45:51Z","timestamp":1773315951000},"score":1,"resource":{"primary":{"URL":"https:\/\/univagora.ro\/jour\/index.php\/ijccc\/article\/view\/7405"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.15837\/ijccc.2026.2.7405","relation":{},"ISSN":["1841-9844","1841-9836"],"issn-type":[{"value":"1841-9844","type":"electronic"},{"value":"1841-9836","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}