KAN-LSTM: Benchmarking Kolmogorov-Arnold Networks for Cyber Security Threat Detection in IoT Networks
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917371898232832 |
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| author | Hassanin, Mohammed |
| author_facet | Hassanin, Mohammed |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28985 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | KAN-LSTM: Benchmarking Kolmogorov-Arnold Networks for Cyber Security Threat Detection in IoT Networks Hassanin, Mohammed Cryptography and Security 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 |
| title | KAN-LSTM: Benchmarking Kolmogorov-Arnold Networks for Cyber Security Threat Detection in IoT Networks |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2603.28985 |