{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T05:08:51Z","timestamp":1787375331012,"version":"3.56.0"},"reference-count":194,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019779","name":"Qatar National Library","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019779","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Industry 5.0 represents a paradigm shift toward human\u2013AI collaboration in manufacturing, incorporating unprecedented volumes of robots, Internet of Things (IoT) devices, Augmented\/Virtual Reality (AR\/VR) systems, and smart devices. This extensive interconnectivity introduces significant cybersecurity vulnerabilities. While AI has proven effective for cybersecurity applications, including intrusion detection, malware identification, and phishing prevention, cybersecurity professionals have shown reluctance toward adopting black-box machine learning solutions due to their opacity. This hesitation has accelerated the development of explainable artificial intelligence (XAI) techniques that provide transparency into AI decision-making processes. This systematic review examines XAI-based intrusion detection systems (IDSs) for Industry 5.0 environments. We analyze how explainability impacts cybersecurity through the critical lens of adversarial XAI (Adv-XIDS) approaches. Our comprehensive analysis of 135 studies investigates XAI\u2019s influence on both advanced deep learning and traditional shallow architectures for intrusion detection. We identify key challenges, opportunities, and research directions for implementing trustworthy XAI-based cybersecurity solutions in high-stakes Industry 5.0 applications. This rigorous analysis establishes a foundational framework to guide future research in this rapidly evolving domain.<\/jats:p>","DOI":"10.3390\/info16121036","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T16:31:52Z","timestamp":1764261112000},"page":"1036","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Explainable AI-Based Intrusion Detection Systems for Industry 5.0 and Adversarial XAI: A Systematic Review"],"prefix":"10.3390","volume":"16","author":[{"given":"Naseem","family":"Khan","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Department, Hamad Bin Khalifa University, Ar-Rayyan 34110, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0931-9275","authenticated-orcid":false,"given":"Kashif","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Munster Technological University Cork, T12 P928 Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aref","family":"Al Tamimi","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4324-1774","authenticated-orcid":false,"given":"Mohammed M.","family":"Alani","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computing Sciences, Rochester Institute of Technology (RIT-Dubai), Dubai P.O. Box 341055, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amine","family":"Bermak","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Hamad Bin Khalifa University, Ar-Rayyan 34110, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Issa","family":"Khalil","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Speith, T. (2022, January 21\u201324). A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea.","DOI":"10.1145\/3531146.3534639"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.future.2023.06.001","article-title":"Survey on Federated Learning enabling indoor navigation for industry 4.0 in B5G","volume":"148","author":"Alsamhi","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rane, N.L., Kaya, \u00d6., and Rane, J. (2024). 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