A Scalable Hybrid Autoencoder–Extreme Learning Machine Framework for Adaptive Intrusion Detection in High-Dimensional Networks

Anubhav Kumar, Rajamani Radhakrishnan, Mani Sumithra, Prabu Kaliyaperumal, Balamurugan Balusamy and Francesco Benedetto ()
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Anubhav Kumar: School of Computer Science and Engineering, Galgotias University, Greater Noida 203201, India
Rajamani Radhakrishnan: School of Computer Science and Engineering, Galgotias University, Greater Noida 203201, India
Mani Sumithra: Department of Information Technology, Panimalar Engineering College, Chennai 600123, India
Prabu Kaliyaperumal: School of Computer Science and Engineering, Galgotias University, Greater Noida 203201, India
Balamurugan Balusamy: Associate Dean-Students, Shiv Nadar University, Delhi-NCR Campus, Noida 201305, India
Francesco Benedetto: Signal Processing for TLC and Economics, University of Roma Tre, 00154 Rome, Italy

Future Internet, 2025, vol. 17, issue 5, 1-18

Abstract: 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–Extreme Learning Machine (AE–ELM) 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.

Keywords: intrusion detection system; autoencoder; extreme learning machine; cloud continuum; edge–fog–cloud orchestration; scalable iot security; real-time threat detection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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