The integration of WiFi fingerprint-based indoor positioning technology and big data analysis emerges as a new research prospect. Through the analysis of big data collected from users’ submission, we can discover many other applications of fingerprint positioning. A popular application is the check-in to point of interest (POI) for its crowd traffic evaluation according to the volume of received signal strength (RSS) fingerprints submitted by users. However, this crowd traffic evaluation method may be susceptible to the intrusion of malicious check-in behaviors. Attackers who are not at the target POI submit the self-modification RSS fingerprints that can be located at the target POI in order to illegally increase its crowd traffic. To this end, we propose a malicious check-in defense scheme based on the access point (AP) selection to resist attackers who aim to successfully initiate the fingerprint modification. Specifically, the distance between different POIs in fingerprint space is firstly developed for AP selection. Then, in order to increase the robustness of selected AP subset, we propose the mutual information among different classes as a selection condition. Through the multiobjective optimization and Pareto optimality, we can obtain the best AP subset to participate in the computation of positioning algorithm. Furthermore, the optimal modified fingerprint is searched by the level set method (LSM), which can be utilized to measure the costs of attackers and the robustness of the system. In addition, we propose an iterative weight updating method based on classification error to learn the optimal weight in order to balance the positioning accuracy and robustness. We finally carry out extensive simulations to validate that the POI crowd traffic can be assessed in terms of the RSS fingerprint-related information and our proposed scheme can perform high robustness to resist malicious check-in.

中文翻译：

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使用室内定位系统的大数据分析防御恶意登机：一种接入点选择方法

基于WiFi指纹的室内定位技术与大数据分析的集成成为新的研究前景。通过分析从用户提交的数据中收集的大数据，我们可以发现指纹定位的许多其他应用。流行的应用程序是根据用户提交的接收信号强度（RSS）指纹的数量，对感兴趣的人群进行签入点（POI）评估。但是，这种人群交通评估方法可能容易受到恶意签入行为的入侵。不在目标POI的攻击者提交可位于目标POI的自修改RSS指纹，以非法增加其人群流量。为此，我们提出了一种基于访问点（AP）选择的恶意签入防御方案，以抵抗旨在成功启动指纹修改的攻击者。具体地，首先开发指纹空间中不同POI之间的距离以用于AP选择。然后，为了提高所选AP子集的鲁棒性，我们提出了不同类别之间的互信息作为选择条件。通过多目标优化和帕累托最优，我们可以获得最佳的AP子集来参与定位算法的计算。此外，通过级别设置方法（LSM）搜索最佳的修改指纹，该指纹可用于衡量攻击者的成本和系统的健壮性。此外，为了平衡定位精度和鲁棒性，我们提出了一种基于分类误差的迭代权重更新方法，以学习最优权重。最后，我们进行了广泛的仿真，以验证可以根据RSS指纹相关信息来评估POI人群流量，并且我们提出的方案可以具有很高的鲁棒性以抵抗恶意签入。