用可学习激活函数的KAN模型提升物联网入侵检测精度与可解释性
Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)
- 采用可学习激活函数的KAN替代传统ML模型
- 在物联网数据集上达到与随机森林相当的准确率
- 兼具高精度与模型可解释性，适合安全敏感场景
物联网（IoT）的指数级增长带来了显著的安全挑战，其网络已成为网络攻击的主要目标。本研究探讨了柯尔莫戈罗夫-阿诺德网络（Kolmogorov-Arnold Networks, KANs）作为传统机器学习模型在物联网入侵检测中的替代方案。KAN通过使用可学习的激活函数，在多个公开物联网数据集上表现出优于传统多层感知机（MLPs）的性能，并在准确率上与随机森林（Random Forest）和梯度提升树（XGBoost）等先进模型相当，同时提供了更优的可解释性，为物联网环境下的安全监控提供了更具透明性的解决方案。
原文摘要 · Abstract (English)
The exponential growth of the Internet of Things (IoT) has led to the emergence of substantial security concerns, with IoT networks becoming the primary target for cyberattacks. This study examines the potential of Kolmogorov-Arnold Networks (KANs) as an alternative to conventional machine learning models for intrusion detection in IoT networks. The study demonstrates that KANs, which employ learnable activation functions, outperform traditional MLPs and achieve competitive accuracy compared to state-of-the-art models such as Random Forest and XGBoost, while offering superior interpretability for intrusion detection in IoT networks.
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