Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services - 2018 PROJECT TITLE :Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services - 2018ABSTRACT:Real-time crowdsourced maps, like Waze give timely updates on traffic, congestion, accidents, and points of interest. During this Project, we have a tendency to demonstrate how lack of sturdy location authentication allows creation of software-based mostly Sybil devices that expose crowdsourced map systems to a selection of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. A lot of importantly, we describe techniques to come up with Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. To defend against Sybil devices, we tend to propose a replacement approach primarily based on co-location edges, authenticated records that attest to the one-time physical co-location of a try of devices. Over time, co-location edges combine to form massive proximity graphs that attest to physical interactions between devices, permitting scalable detection of virtual vehicles. We tend to demonstrate the efficacy of this approach using giant-scale simulations, and the way they will be used to dramatically reduce the impact of the attacks. We have informed Waze/Google team of our analysis findings. Currently, we tend to are in active collaboration with Waze team to improve the safety and privacy of their system. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks - 2018 Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks - 2018
