SafeShareRide: Edge-based Attack Detection in Ridesharing Services

Authors: 

Liangkai Liu and Xingzhou Zhang, Wayne State University; Mu Qiao, IBM Research-Almaden; Weisong Shi, Wayne State University

Abstract: 

Ridesharing services, such as Uber and Didi, have enjoyed great popularity in our daily life. However, it remains a big challenge to guarantee the passenger and driver safety during the rides. In this paper, we propose an edge-based attack detection in ridesharing services, namely SafeShareRide, which can detect dangerous events happening on the vehicle in near real time. The detection of SafeShareRide consists of three stages: speech recognition, driving behavior detection and video capture and analysis. In our preliminary work, we implement the three detection stages by leveraging opensource algorithms and demonstrate the applicability of SafeShareRide. Furthermore, we identify several observations for smart phone based edge computing systems.

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BibTeX
@inproceedings {216783,
title = {{SafeShareRide}: Edge-based Attack Detection in Ridesharing Services},
booktitle = {USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18)},
year = {2018},
address = {Boston, MA},
url = {https://www.usenix.org/conference/hotedge18/presentation/liu},
publisher = {USENIX Association},
month = jul,
}