Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)
It addresses security threats in IoT networks, which are increasingly targeted by cyberattacks, by providing a more interpretable detection method, though it appears incremental as it builds on existing KANs for a specific domain.
This study tackled IoT network intrusion detection by using Kolmogorov-Arnold Networks (KANs) as an alternative to conventional models, achieving competitive accuracy compared to state-of-the-art methods like Random Forest and XGBoost while offering superior interpretability.
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.