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

2025-01-16

AI Summary

Wi-Fi indoor positioning systems suffer from degraded accuracy due to adversarial attacks, particularly signal spoofing and received signal strength (RSS) manipulation. To address this, we propose a robust localization method based on the Kolmogorov–Arnold Network (KAN)—the first application of KAN to defensive indoor positioning. Our approach establishes a KAN-based baseline model and innovatively integrates Projected Gradient Descent (PGD) adversarial training with a dual-model weighted ensemble mechanism. This architecture substantially enhances model stability and generalization under both spoofing and RSS-manipulation attacks: median localization errors are reduced to 2.01 m and 1.975 m, respectively—approximately 10% lower than the baseline—and represent one of the best reported performances for adversarially robust Wi-Fi localization. Our work establishes a novel paradigm for leveraging KANs in security-enhanced positioning systems.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

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.

Problem

• Indoor Wi-Fi positioning
• Signal Spoofing
• Strength Tampering

Innovation

• Anti-Cheating Super-Network
• Kolmogorov-Arnold Network
• Robust Indoor Localization