Driver Profiling and Identification Based on Time Series Analysis

As more and more cars are connected to the internet, the threat of cyber-attacks and illegal access to one's automobile increases expeditiously. Illegal access to automobiles via. the car's integrated network system is quite a common phenomenon nowadays. Thus, it is essential to identify drivers on the basis of their driving patterns. Henceforth, this paper presents a driver profiling and identification method based on data acquired from car sensors. In-vehicle sensors generate dozens of operational data streams, and identifying the right representative features for driver profiling is a challenging task. Therefore, in our work to capture human driving behavior dynamics, we have designed a framework based on Long Short Term Memory. Moreover, for extracting relevant and independent features from the Controller Area Network (CAN) dataset, we suggested using feature selection algorithms. The proposed framework is evaluated on the publicly available vehicle CAN OBD-II dataset. While we demonstrate the effectiveness of the proposed architecture, an essential objective of this study is to verify that inter-driver heterogeneity and intra-driver homogeneity can be modeled using time series dependency.