Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Dheeraj Basavaraj,Shahab Tayeb

doi:10.3390/jsan11010006

Abstract

With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has been a rapid growth in the automotive industry as network-enabled and electronic devices are now integral parts of vehicular ecosystems. These include the development of automobile technologies, namely, Connected and Autonomous Vehicles (CAV) and electric vehicles. Attacks on IoV may lead to malfunctioning of Electronic Control Unit (ECU), brakes, control steering issues, and door lock issues that can be fatal in CAV. To mitigate these risks, there is need for a lightweight model to identify attacks on vehicular systems. In this article, an efficient model of an Intrusion Detection System (IDS) is developed to detect anomalies in the vehicular system. The dataset used in this study is an In-Vehicle Network (IVN) communication protocol, i.e., Control Area Network (CAN) dataset generated in a real-time environment. The model classifies different types of attacks on vehicles into reconnaissance, Denial of Service (DoS), and fuzzing attacks. Experimentation with performance metrics of accuracy, precision, recall, and F-1 score are compared across a variety of classification models. The results demonstrate that the proposed model outperforms other classification models.

Highlights

In the past decades, devices such as cellular phones, vehicles, and home appliances were connected to the Internet due to the advancements in the Internet of things (IoT) [1]
This paper briefly examines different models in its literature review and models using SVM and k-NN algorithm on the Control Area Network (CAN) intrusion dataset generated from the Hacking and Countermeasures Research lab (HCRL)
The Internet of Vehicles (IoV) is gaining popularity as it enables vehicles to communicate with traffic externally and communicate with emergency modules internally

Summary

Introduction

Devices such as cellular phones, vehicles, and home appliances were connected to the Internet due to the advancements in the Internet of things (IoT) [1]. The phenomenal increase in the number of users utilizing IoT devices has made the technology even popular. There has been an increase in the development of vehicular technologies such as Connected Vehicles (CVs), Autonomous Vehicles (AVs), and Electric Vehicles (EVs) [3]. CVs and AVs, i.e., Connected Autonomous Vehicles (CAVs), are expected to reduce parking space, traffic congestion, road accidents, and cost of transportation to a large extent [3]. The combination of these three technologies will enhance the services and operations in a smart city [4]

Methods

Results

Conclusion