Abstract
Today’s modern vehicles are connected to a network and are considered smart objects of IoT, thanks to the capability to send and receive data from the network. One of the greatest challenges in the automotive sector is to make the vehicle secure and reliable. In fact, there are more connected instruments on a vehicle, such as the infotainment system and/or data interchange systems. Indeed, with the advent of new paradigms, such as Smart City and Smart Road, the vision of Internet of Things has evolved substantially. Today, we talk about the V2X systems in which the vehicle is strongly connected with the rest of the world. In this scenario, the main aim of all connected vehicles vendors is to provide a secure system to guarantee the safety of the drive and persons against a possible cyber-attack. So, in this paper, an embedded Intrusion Detection System (IDS) for the automotive sector is introduced. It works by adopting a two-step algorithm that provides detection of a possible cyber-attack. In the first step, the methodology provides a filter of all the messages on the Controller Area Network (CAN-Bus) thanks to the use of a spatial and temporal analysis; if a set of messages are possibly malicious, these are analyzed by a Bayesian network, which gives the probability that a given event can be classified as an attack. To evaluate the efficiency and effectiveness of our method, an experimental campaign was conducted to evaluate them, according to the classic evaluation parameters for a test’s accuracy. These results were compared with a common data set on cyber-attacks present in the literature. The first experimental results, obtained in a test scenario, seem to be interesting. The results show that our method has good correspondence in the presence of the most common cyber-attacks (DDoS, Fuzzy, Impersonating), obtaining a good score relative to the classic evaluation parameters for a test’s accuracy. These results have decreased performance when we test the system on a Free State Attack.
Highlights
Modern vehicles are considered smart objects of an IoT ecosystem [1]
For procedures in the testing phase, it is first necessary to decide which hardware and software components to use in order to test the proposed approach; the classification algorithm and the trained Bayesian network must be implemented
In addition to the simulator, the architecture includes a steering wheel and pedals that allow controlling the vehicle connected to the CAN-Bus through an emulated CAN-Bus; a server that simulates the external environment; an infotainment system that ensures an access point to the CAN-bus; and a board equipped with a system on a chip (SoC) that implements the intrusion detection system
Summary
Modern vehicles are considered smart objects of an IoT ecosystem [1]. Automated and connected vehicles have a complex architecture, as they integrate multiple automated driving functions and a wide variety of communication interfaces [2,3]. Security maintainability: if we want to refer, for example, to the cryptographic protection of data, it is unlikely that the keys and algorithms adopted in the initial phase will guarantee the same level of protection over time For this reason, Security-by-design must be associated with a modular development approach that allows the creation of products capable of adapting to emerging threats. The need to develop such countermeasures is accentuated by the frequent use of technologies borrowed from other sectors, such as OTA and bluetooth connections In this work, it is proposed an intrusion detection system capable of analyzing traffic over the CAN-Bus and of understanding whether the messages that transmit over the communication channel are malicious or not. We discuss the backgrounds of cybersecurity in IoT, machine learning and Bayesian networks, and the automotive sector and CAN-Bus. The section presents our case of study and follows the proposed approach.
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