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

2025-08-07

AI Summary

IoT systems face increasingly severe intrusion threats, yet existing detection models often fail to simultaneously achieve high accuracy and interpretability. This paper proposes a novel IoT intrusion detection framework based on Kolmogorov–Arnold Networks (KANs), which replace fixed nonlinear activation units with learnable, spline-based activation functions—thereby enhancing both representational capacity and structural interpretability. Evaluated on standard IoT benchmark datasets, KANs surpass traditional multilayer perceptrons (MLPs) in accuracy and F1-score, while matching the performance of strong tree-based baselines such as Random Forest and XGBoost. Crucially, KANs provide intrinsic, component-level functional visualizations—offering substantially greater transparency than black-box models. This work establishes a new paradigm for trustworthy IoT security analytics, unifying state-of-the-art detection performance with rigorous model interpretability.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Transparent, Interpretable, Explainable ML
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Enhancing IoT threat detection using KANs
• Comparing KANs with traditional ML models for security
• Improving interpretability in IoT intrusion detection systems

Innovation

• Uses Kolmogorov-Arnold Networks for IoT threat detection
• Employs learnable activation functions for better performance
• Offers superior interpretability compared to traditional models