3 research outputs found
Machine Learning and Location Verification in Vehicular Networks
Location information will play a very important role in emerging wireless
networks such as Intelligent Transportation Systems, 5G, and the Internet of
Things. However, wrong location information can result in poor network
outcomes. It is therefore critical to verify all location information before
further utilization in any network operation. In recent years, a number of
information-theoretic Location Verification Systems (LVSs) have been formulated
in attempts to optimally verify the location information supplied by network
users. Such LVSs, however, are somewhat limited since they rely on knowledge of
a number of channel parameters for their operation. To overcome such
limitations, in this work we introduce a Machine Learning based LVS (ML-LVS).
This new form of LVS can adapt itself to changing environments without knowing
the channel parameters. Here, for the first time, we use real-world data to
show how our ML-LVS can outperform information-theoretic LVSs. We demonstrate
this improved performance within the context of vehicular networks using
Received Signal Strength (RSS) measurements at multiple verifying base
stations. We also demonstrate the validity of the ML-LVS even in scenarios
where a sophisticated adversary optimizes her attack location.Comment: 5 pages, 3 figure
Cybersecurity Measures for Geocasting in Vehicular Cyber Physical System Environments
Geocasting in vehicular communication has witnessed significant attention due to the benefits of location oriented information dissemination in vehicular traffic environments. Various measures have been applied to enhance geocasting performance including dynamic relay area selection, junction nodes incorporation, caching integration, and geospatial distribution of nodes. However, the literature lacks towards geocasting under malicious relay vehicles leading to cybersecurity concern in vehicular traffic environments. In this context, this paper presents Cybersecurity Measures for Geocasting in Vehicular traffic environments (CMGV) focusing on security oriented vehicular connectivity. Specifically, a vehicular intrusion prevention technique is developed to measure the connectivity between the cache agent and cache user vehicles. The connectivity between static transport vehicles and cache agent/cache user is measured via vehicular intrusion detection approach. The performance of the proposed vehicular cybersecurity measure is evaluated in realistic traffic environments. The comparative performance evaluation attests the benefits of security oriented geocasting in vehicular traffic environments