Crowdsourced indoor localization methods have grasped much attention in recent years as a method of reducing the cost of constructing the fingerprint database. In a crowdsourcing environment, however, the localization system is vulnerable to malicious attacks, which possibly lead to serious localization errors. In this paper, we conclude the potential attacks during fingerprint database updates and online inference phases and propose a secure indoor crowdsourced localization system, BERT-ADLOC, based on BLE fingerprints. Our system consists of two main parts: adversarial sample discriminator BERT-AD and indoor localization model BERT-LOC. Our proposed BERT-AD recognizes fake fingerprints during the database update phase, while BERT-LOC defends against attacks online, in which valid beacons are moved or malicious beacons are deployed. A tailored BERT model is introduced to extract deep hidden features through the self-attention mechanism. Our experiments show that BERT-ADLOC achieves a good localization performance against adversaries both in the fingerprint database update phase and online inference phase.

近年来，众包的室内定位方法作为降低构建指纹数据库的成本的方法已经引起了广泛关注。但是，在众包环境中，本地化系统容易受到恶意攻击，这可能会导致严重的本地化错误。在本文中，我们总结了指纹数据库更新和在线推断阶段中的潜在攻击，并提出了一种基于BLE指纹的安全室内众包定位系统BERT-ADLOC。我们的系统包括两个主要部分：对抗性样本鉴别器BERT-AD和室内定位模型BERT-LOC。我们建议的BERT-AD在数据库更新阶段可以识别伪造的指纹，而BERT-LOC可以防御在线攻击，在这种攻击中，有效信标会被移动或部署恶意信标。引入了定制的BERT模型，以通过自我注意机制提取深层的隐藏特征。我们的实验表明，在指纹数据库更新阶段和在线推断阶段，BERT-ADLOC均能针对敌方取得良好的定位性能。