Abstract
Communication between the nodes in a vehicle is performed using many protocols. The most common of these is known as the Controller Area Network (CAN). The functionality of the CAN protocol is based on sending messages from one node to all others throughout a bus. Messages are sent without either source or destination addresses. Consequently, it is simple for an attacker to inject malicious messages. This may lead to some nodes malfunctioning or total system failure, which can affect the safety of the driver as well as the vehicle. Detecting intrusions is a challenging problem in the context of using CAN bus for in-vehicle communication. Most existing work focuses on the physical aspects without taking into consideration the data itself. Machine Learning (ML) tools, especially classification techniques, have been widely used to address similar problems. In this paper, we use and compare several ML techniques to deal with the problem of detecting intrusions in in-vehicle communication. An experimental study is performed using a real dataset extracted from a KIA Soul car. Compared to previous work, which focuses on detecting intrusions based on the physical aspect, this paper aims to concentrate on the application of data analysis and statistical learning techniques. Furthermore, the paper provides a comparative study of the most common ML techniques. The results show that the techniques under consideration in this paper outperform other techniques that have been used previously.
Highlights
A considerable amount of research has focused on vehicle communication technology, such as smart vehicles, Vehicular ad hoc Networks (VANET) [1], [2], and Intelligent Transportation Systems (ITS)
Intravehicular networks have many advantages [5], including (1) reducing the cable budget, which is the third most costly system after the engine and the chassis; (2) minimizing the packaging space by using fewer connections for more electrical and electronic features, allowing a reduction in vehicle size; (3) meeting higher bandwidth demands that can manage the large number of Electronic Control Units (ECUs), with some vehicles containing up to 70 ECUs with 2500 internal signals [5]; and (4) making communication more reliable because bus-based communication is more robust than the traditional point-to-point communication in older vehicles
WORK This paper deals with an important problem: malicious intrusion in communications in vehicles using the Controller Area Network (CAN) bus protocol
Summary
A considerable amount of research has focused on vehicle communication technology, such as smart vehicles, Vehicular ad hoc Networks (VANET) [1], [2], and Intelligent Transportation Systems (ITS). While existing research works have used ML models to deal with this challenging problem, they appear to be insufficient and can be enhanced by using other ML models This motivates us to explore the capabilities of other advanced ML techniques, such as SVM, DT, RF, and MLP to overcome the existing security concerns with in-vehicle CAN buses. The main objectives of the paper are as follows: Develop intrusion detection-based ML in In-Vehicle controller area network bus through applying various ML techniques in the context of in-vehicle CAN bus networks as an IDS. Conduct a comparative performance evaluation of applied ML for intrusion detection in an in-vehicle CAN bus using a set of classifiers on a real dataset that includes messages transmitted using a CAN bus extracted from a KIA Soul car [6].
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