KAN-LSTM: Benchmarking Kolmogorov-Arnold Networks for Cyber Security Threat Detection in IoT Networks

Computer Science > Cryptography and Security [Submitted on 30 Mar 2026] KAN-LSTM: Benchmarking Kolmogorov-Arnold Networks for Cyber Security Threat Detection in IoT Networks Mohammed Hassanin By utilising their adaptive activation functions, Kolmogorov-Arnold Networks (KANs) can be applied in a novel way for the diverse machine learning tasks, including cyber threat detection. KANs substitute conventional linear weights with spline-parametrized univariate functions, which allows them to learn activation patterns dynamically, inspired by the Kolmogorov-Arnold representation theorem. In a network traffic data, we show that KANs perform better than traditional Multi-Layer Perceptrons (MLPs), yielding more accurate results with a significantly less number of learnable parameters. We also propose KAN-LSTM model to combine advantages of spatial and temporal encoding. The suggested methodology highlights the potential of KANs as an effective tool in detecting cyber threats and offers up new directions for adaptive defensive models. Lastly, we conducted experiments on three main dataset, UNSW-NB15, NSL-KDD, and CICID2017, as well as we developed a new dataset combined from IOT-BOT, NSL-KDD, and CICID2017 to present a stable, unbiased, large-scale dataset with diverse traffic patterns. The results show the superiority of KAN-LSTM and then KAN models over the traditional deep learning models. The source code is available at GitHub repository Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.28985 [cs.CR]   (or arXiv:2603.28985v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.28985 Focus to learn more Submission history From: Mohammed Hassanin [view email] [v1] Mon, 30 Mar 2026 20:35:27 UTC (2,775 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)