{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:03:06Z","timestamp":1771516986967,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The rapid expansion of network environments has introduced significant cybersecurity challenges, particularly in handling high-dimensional traffic and detecting sophisticated threats. This study presents a novel, scalable Hybrid Autoencoder\u2013Extreme Learning Machine (AE\u2013ELM) framework for Intrusion Detection Systems (IDS), specifically designed to operate effectively in dynamic, cloud-supported IoT environments. The scientific novelty lies in the integration of an Autoencoder for deep feature compression with an Extreme Learning Machine for rapid and accurate classification, enhanced through adaptive thresholding techniques. Evaluated on the CSE-CIC-IDS2018 dataset, the proposed method demonstrates a high detection accuracy of 98.52%, outperforming conventional models in terms of precision, recall, and scalability. Additionally, the framework exhibits strong adaptability to emerging threats and reduced computational overhead, making it a practical solution for real-time, scalable IDS in next-generation network infrastructures.<\/jats:p>","DOI":"10.3390\/fi17050221","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T06:10:44Z","timestamp":1747289444000},"page":"221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Scalable Hybrid Autoencoder\u2013Extreme Learning Machine Framework for Adaptive Intrusion Detection in High-Dimensional Networks"],"prefix":"10.3390","volume":"17","author":[{"given":"Anubhav","family":"Kumar","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Galgotias University, Greater Noida 203201, India"}]},{"given":"Rajamani","family":"Radhakrishnan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Galgotias University, Greater Noida 203201, India"}]},{"given":"Mani","family":"Sumithra","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Panimalar Engineering College, Chennai 600123, India"}]},{"given":"Prabu","family":"Kaliyaperumal","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Galgotias University, Greater Noida 203201, India"}]},{"given":"Balamurugan","family":"Balusamy","sequence":"additional","affiliation":[{"name":"Associate Dean-Students, Shiv Nadar University, Delhi-NCR Campus, Noida 201305, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9203-1642","authenticated-orcid":false,"given":"Francesco","family":"Benedetto","sequence":"additional","affiliation":[{"name":"Signal Processing for TLC and Economics, University of Roma Tre, 00154 Rome, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Isong, B., Kgote, O., and Abu-Mahfouz, A. 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