{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:08:46Z","timestamp":1779149326257,"version":"3.51.4"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":429,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005712","name":"University of Jordan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005712","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019286","name":"Ajman University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019286","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020328","name":"University of Petra","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020328","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Internet of Things and Cyber-Physical Systems"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.iotcps.2026.03.002","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:59:35Z","timestamp":1773273575000},"page":"143-164","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["AOA-SMA-EGRUAttNet: A hybrid feature selection and dual-stream attention-based intrusion detection framework for IIoT systems"],"prefix":"10.1016","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4442-1865","authenticated-orcid":false,"given":"Yousef","family":"Sanjalawe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1025-7868","authenticated-orcid":false,"given":"Salam","family":"Fraihat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2134-4158","authenticated-orcid":false,"given":"Salam","family":"Al-E'mari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharif Naser","family":"Makhadmeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.iotcps.2026.03.002_bib1","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.procs.2022.12.282","article-title":"Industrial internet of things (iiot): opportunities, challenges, and requirements in manufacturing businesses in emerging economies","volume":"217","author":"Peter","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.iotcps.2026.03.002_bib2","doi-asserted-by":"crossref","first-page":"6647","DOI":"10.3390\/s21196647","article-title":"Recent technologies, security countermeasure and ongoing challenges of industrial internet of things (iiot): a survey","volume":"21","author":"Tan","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.iotcps.2026.03.002_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2025.101536","article-title":"A crossover-integrated marine predator algorithm for feature selection in intrusion detection systems within iot environments","author":"Makhadmeh","year":"2025","journal-title":"Internet Things"},{"key":"10.1016\/j.iotcps.2026.03.002_bib4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3612918","article-title":"A comprehensive survey on collaborative data-access enablers in the iiot","volume":"56","author":"Sun","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.iotcps.2026.03.002_bib5","first-page":"129","article-title":"Industry 4.0: industrial internet of things (iiot)","volume":"117","author":"Munirathinam","year":"2020"},{"key":"10.1016\/j.iotcps.2026.03.002_bib6","doi-asserted-by":"crossref","first-page":"5737","DOI":"10.3390\/s21175737","article-title":"Even lower latency in iiot: evaluation of quic in industrial iot scenarios","volume":"21","author":"Fern\u00e1ndez","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.iotcps.2026.03.002_bib7","series-title":"Remote Monitoring, Needs Analysis for Education and Training","first-page":"54","author":"Hill","year":"2022"},{"key":"10.1016\/j.iotcps.2026.03.002_bib8","doi-asserted-by":"crossref","unstructured":"P. Pandiaraja, K. Karthik, P. Rajesh Kanna, S. M. Pc, K. Muthumanickam, et al., Assessing secure cloud information sharing through authentication and encoded indexing, in: 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), IEEE, pp. 1030\u20131037.","DOI":"10.1109\/I-SMAC61858.2024.10714840"},{"key":"10.1016\/j.iotcps.2026.03.002_bib9","doi-asserted-by":"crossref","first-page":"3211","DOI":"10.1007\/s12652-024-04794-y","article-title":"Exploring the landscape of network security: a comparative analysis of attack detection strategies","volume":"15","author":"Rajesh Kanna","year":"2024","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"10.1016\/j.iotcps.2026.03.002_bib10","article-title":"Differential privacy scheme using laplace mechanism and statistical method computation in deep neural network for privacy preservation","volume":"128","author":"Kumar","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.iotcps.2026.03.002_bib11","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.comcom.2023.06.020","article-title":"Cybersecurity for industrial iot (iiot): threats, countermeasures, challenges and future directions","volume":"208","author":"Mekala","year":"2023","journal-title":"Comput. Commun."},{"key":"10.1016\/j.iotcps.2026.03.002_bib12","doi-asserted-by":"crossref","first-page":"161","DOI":"10.3390\/technologies11060161","article-title":"A comprehensive survey of cybersecurity threats, attacks, and effective countermeasures in industrial internet of things","volume":"11","author":"Alnajim","year":"2023","journal-title":"Technologies"},{"key":"10.1016\/j.iotcps.2026.03.002_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128149","article-title":"Scalable hierarchical balanced clustering-based routing with multipath authentication for secured data transmission in large-scale multicast group communications","author":"Somasundaram","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.iotcps.2026.03.002_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2023.103637","article-title":"Intelligent approaches toward intrusion detection systems for industrial internet of things: a systematic comprehensive review","volume":"215","author":"Nuaimi","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"10.1016\/j.iotcps.2026.03.002_bib15","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/MNET.001.1800479","article-title":"Hybrid intrusion detection system for edge-based iiot relying on machine-learning-aided detection","volume":"33","author":"Yao","year":"2019","journal-title":"IEEE Netw."},{"key":"10.1016\/j.iotcps.2026.03.002_bib16","doi-asserted-by":"crossref","first-page":"2407","DOI":"10.1007\/s11277-024-11607-0","article-title":"An enhanced hybrid intrusion detection using mapreduce-optimized black widow convolutional lstm neural networks","volume":"138","author":"Kanna","year":"2024","journal-title":"Wirel. Pers. Commun."},{"key":"10.1016\/j.iotcps.2026.03.002_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116545","article-title":"Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks","volume":"194","author":"Kanna","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.iotcps.2026.03.002_bib18","article-title":"Unified deep learning approach for efficient intrusion detection system using integrated spatial\u2013temporal features","volume":"226","author":"Kanna","year":"2021","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.iotcps.2026.03.002_bib19","doi-asserted-by":"crossref","unstructured":"R. Latha, R. Bommi, An analysis of intrusion detection systems in iiot, in: 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), IEEE, pp. 1\u201310.","DOI":"10.1109\/ICONSTEM56934.2023.10142458"},{"key":"10.1016\/j.iotcps.2026.03.002_bib20","article-title":"Deep learning approaches for intrusion detection in iiot networks\u2013opportunities and future directions","volume":"12","author":"Vaiyapuri","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.iotcps.2026.03.002_bib21","doi-asserted-by":"crossref","unstructured":"D. Hamouda, M. A. Ferrag, N. Benhamida, H. Seridi, Intrusion detection systems for industrial internet of things: a survey, in: 2021 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS), IEEE, pp. 1\u20138.","DOI":"10.1109\/ICTAACS53298.2021.9715177"},{"key":"10.1016\/j.iotcps.2026.03.002_bib22","article-title":"A graphics processing unit assisted cnn-gru framework for the intrusion detection mechanism in the industrial internet of things","volume":"7","author":"Pr","year":"2025","journal-title":"Eng. Res. Express"},{"key":"10.1016\/j.iotcps.2026.03.002_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.eij.2025.100644","article-title":"Advanced ai-driven intrusion detection for securing cloud-based industrial iot","volume":"30","author":"Qureshi","year":"2025","journal-title":"Egyptian Inform. J."},{"key":"10.1016\/j.iotcps.2026.03.002_bib24","article-title":"A predominant intrusion detection system in iiot using elcg-dsa and lws-biolstm with blockchain","author":"Gudivaka","year":"2025","journal-title":"Sustain. Comput.: Inform. Sys."},{"key":"10.1016\/j.iotcps.2026.03.002_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113337","article-title":"Next-generation iiot security: comprehensive comparative analysis of cnn-based approaches","volume":"316","author":"Dong","year":"2025","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.iotcps.2026.03.002_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2025.111207","article-title":"Industrial iot intrusion attack detection based on composite attention-driven multi-layer pyramid features","volume":"263","author":"Zhai","year":"2025","journal-title":"Comput. Netw."},{"key":"10.1016\/j.iotcps.2026.03.002_bib27","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/9023719","article-title":"Intrusion detection system for industrial internet of things based on deep reinforcement learning","volume":"2022","author":"Tharewal","year":"2022","journal-title":"Wireless Commun. Mobile Comput."},{"key":"10.1016\/j.iotcps.2026.03.002_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123808","article-title":"Deep learning enabled intrusion detection system for industrial iot environment","volume":"249","author":"Nandanwar","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.iotcps.2026.03.002_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102685","article-title":"Advancing network security in industrial iot: a deep dive into ai-enabled intrusion detection systems","volume":"62","author":"Shahin","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.iotcps.2026.03.002_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111785","article-title":"An end-to-end learning approach for enhancing intrusion detection in industrial-internet of things","volume":"294","author":"Hassini","year":"2024","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.iotcps.2026.03.002_bib31","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1007\/s10489-020-01893-z","article-title":"Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems","volume":"51","author":"Hashim","year":"2021","journal-title":"Appl. Intell."},{"key":"10.1016\/j.iotcps.2026.03.002_bib32","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","article-title":"Slime mould algorithm: a new method for stochastic optimization","volume":"111","author":"Li","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.iotcps.2026.03.002_bib33","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1515\/mt-2020-0053","article-title":"Comparision of the political optimization algorithm, the archimedes optimization algorithm and the levy flight algorithm for design optimization in industry","volume":"63","author":"Y\u0131ld\u0131z","year":"2021","journal-title":"Mater. Test."},{"key":"10.1016\/j.iotcps.2026.03.002_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120639","article-title":"New binary archimedes optimization algorithm and its application","volume":"230","author":"Fang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.iotcps.2026.03.002_bib35","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1080\/00207721.2022.2153635","article-title":"Slime mould algorithm: a comprehensive review of recent variants and applications","volume":"54","author":"Chen","year":"2023","journal-title":"Int. J. Syst. Sci."},{"key":"10.1016\/j.iotcps.2026.03.002_bib36","article-title":"Cicapt-iiot: a provenance-based apt attack dataset for iiot environment","author":"Ghiasvand","year":"2024","journal-title":"arXiv preprint arXiv:2407.11278"},{"key":"10.1016\/j.iotcps.2026.03.002_bib37","doi-asserted-by":"crossref","first-page":"40281","DOI":"10.1109\/ACCESS.2022.3165809","article-title":"Edge-iiotset: a new comprehensive realistic cyber security dataset of iot and iiot applications for centralized and federated learning","volume":"10","author":"Ferrag","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.iotcps.2026.03.002_bib38","doi-asserted-by":"crossref","first-page":"3962","DOI":"10.1109\/JIOT.2021.3102056","article-title":"X-iiotid: a connectivity-agnostic and device-agnostic intrusion data set for industrial internet of things","volume":"9","author":"Al-Hawawreh","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.iotcps.2026.03.002_bib39","doi-asserted-by":"crossref","first-page":"6822","DOI":"10.1109\/JIOT.2019.2912022","article-title":"Machine learning-based network vulnerability analysis of industrial internet of things","volume":"6","author":"Zolanvari","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.iotcps.2026.03.002_bib40","series-title":"Wustl-iiot-2021","author":"Zolanvari","year":"2021"},{"key":"10.1016\/j.iotcps.2026.03.002_bib41","doi-asserted-by":"crossref","first-page":"98516","DOI":"10.1109\/ACCESS.2023.3313630","article-title":"Abnormal transactions detection in the ethereum network using semi-supervised generative adversarial networks","volume":"11","author":"Sanjalawe","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.iotcps.2026.03.002_bib42","article-title":"Detection of obfuscated tor traffic based on bidirectional generative adversarial networks and vision transform","volume":"135","author":"Sanjalawe","year":"2023","journal-title":"Comput. Secur."},{"key":"10.1016\/j.iotcps.2026.03.002_bib43","first-page":"257","volume":"28","author":"Alzubi","year":"2025"}],"container-title":["Internet of Things and Cyber-Physical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667345226000027?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667345226000027?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T23:33:44Z","timestamp":1779147224000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2667345226000027"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":43,"alternative-id":["S2667345226000027"],"URL":"https:\/\/doi.org\/10.1016\/j.iotcps.2026.03.002","relation":{},"ISSN":["2667-3452"],"issn-type":[{"value":"2667-3452","type":"print"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AOA-SMA-EGRUAttNet: A hybrid feature selection and dual-stream attention-based intrusion detection framework for IIoT systems","name":"articletitle","label":"Article Title"},{"value":"Internet of Things and Cyber-Physical Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.iotcps.2026.03.002","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.","name":"copyright","label":"Copyright"}]}}