A Lightweight Intrusion Detection Method of Drone Network with Interpretability
WANG Peng
GUO Xiangke
SONG Yafei
WANG Xiaodan
Abstract：Aimed at the problems that computational power is limit,storage space is small,and real-time is high for requirements of drones,a method of detecting interpretable drone network intrusion based on Kolmogorov-Arnold Networks(KAN),called KIDS,is proposed.Under the inspiration of Kolmogorov-Arnold Representation Theorem,KAN is to utilize spline-parameterized univariate functions for replacing the traditional linear weights to dynamically learn activation patterns,enabling effective handling of feature extraction,and achieving excellent drone network intrusion detection performance with a more lightweight network structure.Furthermore,the visualization of parameterized spline functions provides insights into the model's decision-making process during traffic feature extraction,enhancing trust in the model's ap-plication.The extensive experiments being over on the real-world drone network traffic dataset drone-IDS-2020,the results demonstrate that the KIDS achieves superior detection performance by still lower model complexity,and exhibits obvious generalization capability in intrusion detection for surpassing type of drones.
Keywords：drone network intrusion detectionKolmogorov-Arnold Networksinterpretabilitycyber security
Publication Date：2026-02-28
Online Publishing Date：2026-03-13(First online date of this platform, not the publication date of the document)
Pages：11( 106-116 )