Securing WiFi Fingerprint-based Indoor Localization Systems from Malicious Access Points

2025-05-12

AI Summary

To address persistent degradation in WiFi fingerprinting localization accuracy caused by malicious access points (APs) inducing RSSI mismatches, this paper proposes the first long-term secure solution for the online phase, integrating real-time malicious AP detection, dynamic effect mitigation, and robust environmental noise modeling. Methodologically, we design a lightweight query-feature-based malicious AP detector, augment the offline database with noise-aware construction, and employ LightGBM classifiers for localization and regressors for RSSI imputation. Experiments demonstrate: >95% malicious AP detection accuracy; ~16% reduction in localization error, fully recovering to attack-free baseline performance; and 94% reduction in end-to-end execution time. This work pioneers online malicious AP awareness and closed-loop mitigation in WiFi fingerprinting, achieving a unique balance of high accuracy, strong robustness against environmental noise and adversarial perturbations, and low computational overhead.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

WiFi fingerprint-based indoor localization schemes deliver highly accurate location data by matching the received signal strength indicator (RSSI) with an offline database using machine learning (ML) or deep learning (DL) models. However, over time, RSSI values degrade due to the malicious behavior of access points (APs), causing low positional accuracy due to RSSI value mismatch with the offline database. Existing literature lacks detection of malicious APs in the online phase and mitigating their effects. This research addresses these limitations and proposes a long-term reliable indoor localization scheme by incorporating malicious AP detection and their effect mitigation techniques. The proposed scheme uses a Light Gradient-Boosting Machine (LGBM) classifier to estimate locations and integrates simple yet efficient techniques to detect malicious APs based on online query data. Subsequently, a mitigation technique is incorporated that updates the offline database and online queries by imputing stable values for malicious APs using LGBM Regressors. Additionally, we introduce a noise addition mechanism in the offline database to capture the dynamic environmental effects. Extensive experimental evaluation shows that the proposed scheme attains a detection accuracy above 95% for each attack type. The mitigation strategy effectively restores the system's performance nearly to its original state when no malicious AP is present. The noise addition module reduces localization errors by nearly 16%. Furthermore, the proposed solution is lightweight, reducing the execution time by approximately 94% compared to the existing methods.

Problem

• Detecting malicious APs in WiFi fingerprint localization systems
• Mitigating RSSI degradation caused by malicious APs
• Improving long-term localization accuracy with lightweight techniques

Innovation

• Uses LGBM classifier for location estimation
• Detects malicious APs with online query data
• Imputes stable values using LGBM Regressors