Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures
The paper discusses advancements in autonomous online intrusion detection systems (IDS) for IoT devices. It highlights the replication of the AOC-IDS model and identifies key limitations while proposing improvements. The proposed methods demonstrate significant accuracy gains and enhanced deployability on IoT edge devices.
- ▪The AOC-IDS model was replicated on the UNSW-NB15 benchmark, achieving 89.39% accuracy.
- ▪Four key limitations were identified: class imbalance, unreliable pseudo-label generation, limited generalization, and computational overhead.
- ▪The XGBoost-BalSamp method achieved 95.45% accuracy, a 6.26% improvement over the baseline.
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Computer Science > Cryptography and Security arXiv:2605.26166 (cs) [Submitted on 24 May 2026] Title:Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Authors:Hanzala Afzaal, Danish Memon, Chouhdary Bilal Raza, Muhammad Khurram Shahzad View a PDF of the paper titled Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures, by Hanzala Afzaal and 3 other authors View PDF HTML (experimental) Abstract:The rapid proliferation of Internet of Things (IoT) devices has created an urgent demand for adaptive, resource-efficient Intrusion Detection Systems (IDS) capable of handling dynamic and evolving cyber threats.
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