CAN Bus Messages Research Articles

A vehicle firmware security vulnerability: an IVI exploitation

In the last years, the increasing vehicular technology has led to a surge in cybersecurity attacks, particularly regarding connected vehicles and their vulnerable infotainment systems. This paper explores the vulnerabilities within an In-Vehicle Infotainment (IVI) system firmware, focusing on the Gen5W_L multimedia device utilized in Hyundai, Kia, and Genesis vehicles. Leveraging reverse engineering techniques, the study uncovers several security issues within the firmware, allowing for the creation and installation of custom firmware. The paper introduces Chimaera, a reverse engineering attack targeting the IVI system firmware, which exploits a printf vulnerability introduced by statically compiling the mbedtls library. After a reverse engineering analysis and injecting malicious assembly code directly in the IVI firmware, the attack enables the extraction of sensitive information from the firmware files like the cryptographic keys to install a customized firmware. The research shows that exploiting these vulnerabilities facilitates the installation of insecure firmware, granting attackers unauthorized access to vehicle functionalities. This includes the possibility to inject CAN bus messages, potentially compromising critical vehicle systems such as radio controls and safety features. The findings underscore the need for robust cybersecurity measures within automotive systems and highlight the significance of vulnerability assessment to mitigate security risks in connected vehicles.

In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms

Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel implementation of an IDS for in-vehicle security networks based on the concept of multi-scale histograms, which capture the frequencies of message identifiers in CAN-bus in-vehicle networks. In comparison to existing approaches in the literature based on a single histogram, the proposed approach widens the informative context used by the IDS for traffic analysis by taking into consideration sequences of two and three CAN-bus messages to create multi-scale dictionaries. The histograms are created from windows of in-vehicle network traffic. A preliminary multi-scale histogram model is created using only legitimate traffic. Against this model, the IDS performs traffic analysis to create a feature space based on the correlation of the histograms. Then, the created feature space is given in input to a Convolutional Neural Network (CNN) for the identification of the windows of traffic where the attack is present. The proposed approach has been evaluated on two different public data sets achieving a very competitive performance in comparison to the literature.

On the Application of Entropy Measures with Sliding Window for Intrusion Detection in Automotive In-Vehicle Networks.

The evolution of modern automobiles to higher levels of connectivity and automatism has also increased the need to focus on the mitigation of potential cybersecurity risks. Researchers have proven in recent years that attacks on in-vehicle networks of automotive vehicles are possible and the research community has investigated various cybersecurity mitigation techniques and intrusion detection systems which can be adopted in the automotive sector. In comparison to conventional intrusion detection systems in large fixed networks and ICT infrastructures in general, in-vehicle systems have limited computing capabilities and other constraints related to data transfer and the management of cryptographic systems. In addition, it is important that attacks are detected in a short time-frame as cybersecurity attacks in vehicles can lead to safety hazards. This paper proposes an approach for intrusion detection of cybersecurity attacks in in-vehicle networks, which takes in consideration the constraints listed above. The approach is based on the application of an information entropy-based method based on a sliding window, which is quite efficient from time point of view, it does not require the implementation of complex cryptographic systems and it still provides a very high detection accuracy. Different entropy measures are used in the evaluation: Shannon Entropy, Renyi Entropy, Sample Entropy, Approximate Entropy, Permutation Entropy, Dispersion and Fuzzy Entropy. This paper evaluates the impact of the different hyperparameters present in the definition of entropy measures on a very large public data set of CAN-bus traffic with millions of CAN-bus messages with four different types of attacks: Denial of Service, Fuzzy Attack and two spoofing attacks related to RPM and Gear information. The sliding window approach in combination with entropy measures can detect attacks in a time-efficient way and with great accuracy for specific choices of the hyperparameters and entropy measures.

Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles.

Modelling the dynamic behaviour of heavy vehicles, such as buses or trucks, can be very useful for driving simulation and training, autonomous driving, crash analysis, etc. However, dynamic modelling of a vehicle is a difficult task because there are many subsystems and signals that affect its behaviour. In addition, it might be hard to combine data because available signals come at different rates, or even some samples might be missed due to disturbances or communication issues. In this paper, we propose a non-invasive data acquisition hardware/software setup to carry out several experiments with an urban bus, in order to collect data from one of the internal communication networks and other embedded systems. Subsequently, non-conventional sampling data fusion using a Kalman filter has been implemented to fuse data gathered from different sources, connected through a wireless network (the vehicle’s internal CAN bus messages, IMU, GPS, and other sensors placed in pedals). Our results show that the proposed combination of experimental data gathering and multi-rate filtering algorithm allows useful signal estimation for vehicle identification and modelling, even when data samples are missing.

Research on Car Reversing Warning System Based on CAN Bus

The paper study the software and hardware principles of ultrasonic ranging reversing radar, design the vehicle reversing radar based on intelligent node of CAN bus, realize data communication and resource sharing with other intelligent nodes of the reversing radar. The experimental results show that, the design can achieve the expected technical indicators design, measurement precision and the reliability is high, which has certain guiding significance in the design of the car reversing radar, has wide application prospect and practical significance in the function of the extended CAN bus communication messages design and automotive CAN network.

The use of CAN-Bus messages of an agricultural tractor for monitoring its operation

The development of electronic components in engine regulation leads to the possibility of obtaining a large amount of parameters of control processes. Nowadays, these data can be read in an easy way due to the properties of used networks. The data obtained from the data bus must be processed carefully; otherwise, there is a risk of erroneous results. The article presents the results of measurements performed on four tractors. We focused on comparing the fuel consumption of engines and the accuracy of flow measurement readings from the CAN-Bus of these tractors. Even the same engines may show considerable differences in fuel consumption, taken from the CAN-Bus. The maximum deviation of the measured values ranged up to around 40%. However, the value of about 41% was measured in the range of fuel consumption of around 10 kg/h, the error of that tractor at the consumption of about 40 kg/h fell to 1.5 kg/h. A significant difference occurred in the tractor with a 235 kW engine. The consumption in the range of 50 kg/h showed an error of 8.4 kg/h. An improvement of the results can be achieved using derived regression functions. Based on our experience with the measurement of other types of tractors, we know that a similar situation with the accuracy of CAN-Bus data is also an issue in other categories of vehicles.

The usage of CAN-Bus messages for engine power determination

Determination of the actual power of tractor's engine in the operation can be done by calculation which requires the use of a range of parameters such as coefficient of rolling resistance, mechanical efficiency, the moments of inertia, etc. Their values are usually tabulated and therefore the engine power cannot be determine without simplifying. Another solution is to use the actual engine torque message from the CAN-Bus, which brings a specific value of the actual torque. The aim is to use the current torque to calculate the engine power in the deployment of tractor's set in transport operation. The results show that at a uniform movement of the flat section the engine power reached 73 kW. When driving uphill, the value of the actual power reached from 130 to 150 kW depending on the selected gear. Using the actual parameters of torque makes possible to identify the known full speed characteristics of the current engine without the need for assembly demanding measurement techniques.

Robotic Arm Controlled by CAN Bus

This paper describes control of L601-KT robotic arm via embedded microcontroller and industrial bus. This application is composed from two nodes connected to CAN Bus. First node control desired position of whole robotic arm or servo-drives separately. Node subsequently sends these positions to robotic arm via CAN bus. Second node serves as robotic arm CAN bus interface. Node receives CAN Bus messages, calculates the positions for PWM values from desired positions, and sets appropriate servo-drive to desired position.