{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:35:50Z","timestamp":1780356950474,"version":"3.54.1"},"reference-count":25,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This paper proposes an intelligent and adaptive security framework for internet of things (IoT) environments by integrating edge Honeypots, AI\u2010driven intrusion detection systems (IDS), and software\u2010defined networking (SDN). The system is designed to enhance real\u2010time threat detection, reduce false positives, and dynamically mitigate attacks. Edge Honeypots are deployed at the network perimeter to attract and analyze malicious traffic, which is then used to train AI\u2010based IDS models. The IDS employ a hybrid detection mechanism combining signature\u2010based and anomaly\u2010based techniques. SDN facilitates centralized traffic control and dynamic rule updates, enabling rapid responses to new attack vectors. The framework is implemented and evaluated in a simulated SDN\u2010IoT environment using Mininet, with several machine learning models benchmarked. The Decision Tree model achieves the highest detection accuracy (97%) for IoT threats. Experimental results demonstrate improved detection performance, reduced false positives, and enhanced adaptability through a continuous learning loop between the IDS and honeypots.<\/jats:p>","DOI":"10.1002\/cpe.70457","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T02:53:31Z","timestamp":1770087211000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AI\u2010Driven Threat Detection: Synergizing Edge Honeypots and IDS in SDN\u2010Enabled Edge Computing"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7000-6816","authenticated-orcid":false,"given":"Afef","family":"Slimani","sequence":"first","affiliation":[{"name":"Faculty of Sciences of Tunis University Campus  Tunis Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamel","family":"Karoui","sequence":"additional","affiliation":[{"name":"National Institute of Applied Sciences and Technology (INSAT)  Centre Urbain Nord, Tunis Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2017.11.002"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","DOI":"10.1051\/e3sconf\/202449104018","article-title":"A Survey of Security Challenges, Attacks in IoT","volume":"491","author":"Prakash R.","year":"2024","journal-title":"E3S Web of Conferences"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.procs.2024.04.034","article-title":"A Review of IoT Security: Machine Learning and Deep Learning Based Solutions","volume":"235","author":"Dubey K.","year":"2024","journal-title":"Procedia Computer Science"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3555308"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","first-page":"730","DOI":"10.3390\/iot5040033","article-title":"An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices","volume":"5","author":"Omar A. H. E.","year":"2024","journal-title":"IoT"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.55056\/jec.607"},{"key":"e_1_2_10_8_1","volume-title":"Proceedings of the 2024 IEEE International Conference on Industry 40, Artificial Intelligence, and Communications Technology (IAICT)","author":"Vivek V.","year":"2024"},{"issue":"1","key":"e_1_2_10_9_1","first-page":"45","article-title":"Deep Learning Approaches for Intelligent Intrusion Detection in IoT Networks","volume":"12","author":"Wang L.","year":"2024","journal-title":"Journal of Cybersecurity and Privacy"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108626"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110215","article-title":"Review and Analysis of Recent Advances in Intelligent Network Softwarization for the Internet of Things","volume":"241","author":"Zormati M. A.","year":"2024","journal-title":"Computer Networks"},{"issue":"1","key":"e_1_2_10_12_1","first-page":"78","article-title":"Dynamic Network Management Using SDN: Challenges and Solutions","volume":"21","author":"Kim J.","year":"2024","journal-title":"IEEE Transactions on Network and Service Management"},{"issue":"2","key":"e_1_2_10_13_1","first-page":"245","article-title":"High\u2010Interaction Honeypots for Security Research: A Survey and Analysis","volume":"15","author":"Zhang W.","year":"2019","journal-title":"Journal of Cybersecurity"},{"issue":"2","key":"e_1_2_10_14_1","first-page":"56","article-title":"Low\u2010Interaction Honeypots: A Comprehensive Survey and Future Directions","volume":"19","author":"Fenzl B.","year":"2020","journal-title":"Journal of Cyber Defense"},{"issue":"1","key":"e_1_2_10_15_1","first-page":"123","article-title":"Integrating Machine Learning With Honeypots for Enhanced Threat Detection","volume":"20","author":"Vishwakarma A.","year":"2019","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2018.03367731"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/info14010041"},{"key":"e_1_2_10_18_1","volume-title":"IEEE European Symposium on Security and Privacy","author":"Papernot N.","year":"2018"},{"issue":"3","key":"e_1_2_10_19_1","first-page":"456","article-title":"Integrating SDN With Security Mechanisms: Intrusion Detection and Honeypots","volume":"18","author":"Zhang J.","year":"2024","journal-title":"Journal of Cybersecurity"},{"key":"e_1_2_10_20_1","article-title":"Securing IoT and SDN Systems Using Deep\u2010Learning Based Automatic Two\u2010Level Intrusion Detection System (SATIDS)","volume":"140","author":"Elsayed R. 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