Adversarial-Ensemble Kolmogorov Arnold Networks for Enhancing Indoor Wi-Fi Positioning: A Defensive Approach Against Spoofing and Signal Manipulation Attacks

TL;DR

This paper introduces an ensemble of Kolmogorov-Arnold Networks trained with adversarial techniques to improve indoor Wi-Fi positioning robustness against spoofing and signal manipulation attacks, achieving significant error reduction.

Contribution

It proposes a novel ensemble approach using Kolmogorov-Arnold Networks with adversarial training to enhance indoor Wi-Fi positioning security and accuracy.

Findings

Robust model reduces error by ~10% over baseline.

Ensemble model further reduces errors under attack.

Achieves errors around 2 meters in adversarial scenarios.

Abstract

The research presents a study on enhancing the robustness of Wi-Fi-based indoor positioning systems against adversarial attacks. The goal is to improve the positioning accuracy and resilience of these systems under two attack scenarios: Wi-Fi Spoofing and Signal Strength Manipulation. Three models are developed and evaluated: a baseline model (M_Base), an adversarially trained robust model (M_Rob), and an ensemble model (M_Ens). All models utilize a Kolmogorov-Arnold Network (KAN) architecture. The robust model is trained with adversarially perturbed data, while the ensemble model combines predictions from both the base and robust models. Experimental results show that the robust model reduces positioning error by approximately 10% compared to the baseline, achieving 2.03 meters error under Wi-Fi spoofing and 2.00 meters under signal strength manipulation. The ensemble model further outperforms with errors of 2.01 meters and 1.975 meters for the respective attack types. This analysis highlights the effectiveness of adversarial training techniques in mitigating attack impacts. The findings underscore the importance of considering adversarial scenarios in developing indoor positioning systems, as improved resilience can significantly enhance the accuracy and reliability of such systems in mission-critical environments.