Security in Wireless Powertrain Networking through Machine Learning Localization 2019-01-1046
This paper demonstrates a solution to the security problem for automotive wireless powertrain networking. That is, the security for wireless automotive networking requires a localization function before we allow a node to join the network. We explain why for powertrain wireless networking, this ability of identifying the precise location of a communicating wireless node is critical. In this paper, we explore existing methods that others have used to implement localization for wireless networking. Then, we apply machine-learning techniques to a dataset that has localization information associated with received signal strength indication. We reveal insights provided by our dataset though an exploration with statistics and visualization. We then present our problem in terms of pattern recognition via multiple techniques, including Naïve Bayes Classifier and Artificial Neural Networks. Through these techniques, we use our exemplary vehicle network and dataset to demonstrate a simple solution that has excellent performance in determining localization. This is an elegant solution to the security problem for wireless automotive networking.