Benchmarking Classical and Quantum Machine Learning for Intrusion Detection Across Multiple Datasets

Reported by arXiv cs.CR (Taha M. Mahmoud, Naima Kaabouch)

Summary by Cyber Security News27 Oct 2026, 05:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.33119

arXiv:2610.33119v1 Announce Type: new Abstract: Intrusion detection is examined through a comparative benchmark of classical and quantum machine learning models on three datasets: NSL-KDD, UNSW-NB15, and MQTTEEB-D2025. The analysis focuses on how preprocessing decisions, feature selection strategies, and quantum encoding schemes shape model performance on data with differing levels of noise and complexity.

Read the full article at arXiv cs.CR →

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