Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles

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.

SDAE+Bi-LSTM-Based Situation Awareness Algorithm for the CAN Bus of Intelligent Connected Vehicles

“…(2) In terms of the FPR, our SDBL algorithm had the best and most stable performance, followed by GRU-SAE, while the worst was RNN-PCA. (3) In terms of the FNR, the five algorithms had the smallest performance difference on the two datasets. Contrasting the five algorithms, SDBL and GRU-SAE had the best stability.…”

“…With the development of the Internet of Things and the Industrial Internet, an increasing number of intelligent devices are being assembled into traditional cars to form intelligent connected vehicles (ICVs) [1][2][3]. Due to this deepening connection with the internet, cyber-attacks can invade the ICVs from the internet.…”

Cybersecurity in the Internet of Things in Industrial Management

“…In so doing, there is an attempt to secure the automotive industry by developing an encryption system that is able to disguise data to those other than the user and detect any set of actions that aim to compromise the vehicle. In particular, an intrusion detection system capable of analyzing traffic over the Controller Area Network (CAN-Bus) and of understanding whether the messages that transmit over the communication channel are malicious or not [64][65][66].…”

A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics

“…Continuous evaluation over time is not considered in this proposal, and no enhancements of the development process are generated. On the other hand, Pascale et al [ 67 ] proposed the analysis in both spatial and temporal dimensions for intrusion detection.…”