Abnormal Crowd Traffic Detection for Crowdsourced Indoor Positioning in Heterogeneous Communications Networks

WiFi fingerprint-based indoor positioning system emerges to provide fundamental location-related service in heterogeneous communications networks. It relies on crowdsourcing technology in the collection of received signal strength (RSS) to dynamically update fingerprint database. However, this crowdsourced indoor positioning system is vulnerable to the intrusion of dishonest users (i.e., attackers). Attackers may manipulate the number of users who submit RSS fingerprints, and finally mislead the evaluation of crowd traffic evaluation. In this paper, we propose an abnormal crowd traffic detection (ACTD) scheme to identify attackers according to their abnormal RSS sensing behaviors. Specifically, a fog server is explored to serve as the crowdsourcing platform to perform data storage and detection. We first categorize attackers into three different levels according to their real geographical locations and collusion. Then, through the analysis of pseudonym changing behavior in RSS submission, we propose a rarity-based outlier detection to classify attackers of level-1. Furthermore, we propose a variable-length Markov model, i.e., probabilistic suffix tree (PST), to detect the colluded users who are not at the target point of interest (POI). In addition, a metric learning algorithm is developed to detect the collusion of AP organizer based on RSS fingerprint distance difference. The extensive simulation results show that the ACTD scheme can effectively resist attackers with high accuracy and appropriately deal with traffic evaluation from RSS fingerprint information.