Automobile Theft Detection by Clustering Owner Driver Data

TL;DR

This paper presents a novel method for automobile theft detection that uses clustering of owner driving data with k-means, achieving high accuracy without needing theft data for training.

Contribution

The study introduces a theft detection approach based solely on owner driving data, eliminating the need for labeled theft data and demonstrating high accuracy.

Findings

Achieved 99% accuracy in theft detection

Effective clustering of owner driving patterns

No need for theft driving data in training

Abstract

As automobiles become intelligent, automobile theft methods are evolving intelligently. Therefore automobile theft detection has become a major research challenge. Data-mining, biometrics, and additional authentication methods have been proposed to address automobile theft, in previous studies. Among these methods, data-mining can be used to analyze driving characteristics and identify a driver comprehensively. However, it requires a labeled driving dataset to achieve high accuracy. It is impractical to use the actual automobile theft detection system because real theft driving data cannot be collected in advance. Hence, we propose a method to detect an automobile theft attempt using only owner driving data. We cluster the key features of the owner driving data using the k-means algorithm. After reconstructing the driving data into one of these clusters, theft is detected using an error from the original driving data. To validate the proposed models, we tested our actual driving data and obtained 99% accuracy from the best model. This result demonstrates that our proposed method can detect vehicle theft by using only the car owner's driving data.