利用Kolmogorov-Arnold网络（KANs）优化物联网威胁检测
Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)
AI总结：
本研究探索KANs作为物联网入侵检测的替代模型，其可学习激活函数使其优于MLPs，并达到与随机森林和XGBoost相当的准确性，同时提供更强的可解释性。
摘要：
物联网（IoT）的指数级增长引发了严重的安全问题，物联网网络已成为网络攻击的主要目标。本研究探讨了Kolmogorov-Arnold网络（KANs）作为传统机器学习模型在物联网网络入侵检测中的替代方案的潜力。研究表明，采用可学习激活函数的KANs优于传统MLPs，并在与随机森林和XGBoost等最先进模型相比时实现了具有竞争力的准确性，同时为物联网网络中的入侵检测提供了更优的可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
