Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)

Authors: Natalia Emelianova, Carlos Kamienski, Ronaldo C. Prati
Published: 2025-08-07

Source: https://arxiv.org/abs/2508.05591

PDF: https://arxiv.org/pdf/2508.05591

AIにより推定されたラベル

性能評価手法 IoTネットワーク攻撃 透かし設計

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Abstract

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.