Machine Learning-Based Anomaly Detection for Securing In-Vehicle Networks

Abstract

In-vehicle networks (IVNs) are networks that allow communication between different electronic components in a vehicle, such as infotainment systems, sensors, and control units. As these networks become more complex and interconnected, they become more vulnerable to cyber-attacks that can compromise safety and privacy. Anomaly detection is an important tool for detecting potential threats and preventing cyber-attacks in IVNs. The proposed machine learning-based anomaly detection technique uses deep learning and feature engineering to identify anomalous behavior in real-time. Feature engineering involves selecting and extracting relevant features from the data that are useful for detecting anomalies. Deep learning involves using neural networks to learn complex patterns and relationships in the data. Our experiments show that the proposed technique have achieved high accuracy in detecting anomalies and outperforms existing state-of-the-art methods. This technique can be used to enhance the security of IVNs and prevent cyber-attacks that can have serious consequences for drivers and passengers.