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 the 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.

翻译：基于WiFi指纹的室内定位方案通过将接收信号强度指示（RSSI）与离线数据库进行匹配，并利用机器学习（ML）或深度学习（DL）模型实现高精度定位。然而，随着时间的推移，接入点（AP）的恶意行为会导致RSSI值劣化，造成其与离线数据库不匹配，从而降低定位精度。现有文献缺乏在线阶段对恶意AP的检测及其影响缓解机制的研究。本文针对这些局限性，提出一种融合恶意AP检测与影响缓解技术的长期可靠室内定位方案。该方案采用轻量梯度提升机（LGBM）分类器进行位置估计，并集成简洁高效的在线查询数据处理技术以检测恶意AP。随后，通过LGBM回归器为恶意AP注入稳定值以更新离线数据库与在线查询，实现影响缓解。此外，我们在离线数据库中引入噪声添加机制以捕捉动态环境效应。大量实验评估表明：所提方案对各类攻击的检测准确率超过95%；缓解策略能将系统性能恢复至近乎无恶意AP存在的原始状态；噪声添加模块使定位误差降低约16%。该方案具有轻量化特性，与现有方法相比执行时间减少约94%。