Controller Area Network Research Articles

Deep Learning-Based Intrusion Detection System for Internet of Vehicles

The growth of the Internet of Things (IoT) has resulted in several revolutionary applications, such as smart cities, cyber-physical systems, and the Internet of vehicles (IoV). Within the IoV infrastructure, vehicles are comprised of various electronic intelligent sensors or devices used to obtain data and communicate the necessary information with their surroundings. One of the major concerns about the implementation of these sensors or devices is data vulnerability; thus, it is necessary to present a solution that provides security, trust, and privacy to communicating entities and to secure vehicle data from malicious entities. In modern vehicles, the controller area network (CAN) is a fundamental scheme for controlling the interaction among different in-vehicle network sensors. However, not enough security features are present that support data encryption, authorization, and authentication mechanisms to secure the network from cyber or malicious intrusions such as denial of service and fuzzy attacks. An intrusion detection system is presented in this work based on the deep learning architecture to protect the CAN bus in vehicles. The VGG architecture is used and trained for different network intrusion patterns in order to detect malicious attacks. The experiments are performed using the CAN-intrusion-dataset. The experimental findings demonstrate that the presented deep learning system significantly reduces the false positive rate (FPR) compared to the conventional machine learning techniques. The overall accuracy of the system is 96% with FPR of 0.6%.

Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks

Due to the lack of CAN frame encryption and authentication, CAN bus is vulnerable to various attacks, which can in general be divided into message injection, suspension, and falsification. Existing CAN bus anomaly detection mechanisms either can only detect one or two of these attacks, or require numerous CAN messages during predictions, which can hardly realize real-time performance. In this paper, we propose a CAN bus anomaly detection system that can detect all these attacks simultaneously in as short as 3 milliseconds (ms) based on Graph Neural Network (GNN). This work generates directed attributed graphs based on CAN message streams in given message intervals. Node attributes denote data contents in CAN messages while each edge attribute represents the frequency of a typical CAN ID pair in the given interval. Afterwards, a GNN is trained based on generated CAN message graphs. Considering highly imbalanced training data, a two-stage classifier cascade is developed in this paper, which is composed of a one-class classifier for anomaly detection and a multi-class classifier for attack classification. An openmax layer is further introduced to the multi-class classifier to tackle new anomalies from unknown classes. To take advantage of crowdsourcing while protecting user data privacy, we adopt federated learning to train a universal model that covers different driving scenarios and vehicle states. Extensive experiment results show the effectiveness and efficiency of our methodology.

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Driver identification in intelligent transport systems has immense demand, considering the safety and convenience of traveling in a vehicle. The rapid growth of driver assistance systems (DAS) and driver identification system propels the need for understanding the root causes of automobile accidents. Also, in the case of insurance, it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing. It is observed that drivers with frequent records of paying “fines” are compelled to pay higher insurance payments than drivers without any penalty records. Thus driver identification act as an important information source for the intelligent transport system. This study focuses on a similar objective to implement a machine learning-based approach for driver identification. Raw data is collected from in-vehicle sensors using the controller area network (CAN) and then converted to binary form using a one-hot encoding technique. Then, the transformed data is dimensionally reduced using the Principal Component Analysis (PCA) technique, and further optimal parameters from the dataset are selected using Whale Optimization Algorithm (WOA). The most relevant features are selected and then fed into a Convolutional Neural Network (CNN) model. The proposed model is evaluated against four different use cases of driver behavior. The results show that the best prediction accuracy is achieved in the case of drivers without glasses. The proposed model yielded optimal accuracy when evaluated against the K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) models with and without using dimensionality reduction approaches.

Enhancing Road Safety through Accurate Detection of Hazardous Driving Behaviors with Graph Convolutional Recurrent Networks

Car accidents remain a significant public safety issue worldwide, with the majority of them attributed to driver errors stemming from inadequate driving knowledge, non-compliance with regulations, and poor driving habits. To improve road safety, Driving Behavior Detection (DBD) systems have been proposed in several studies to identify safe and unsafe driving behavior. Many of these studies have utilized sensor data obtained from the Controller Area Network (CAN) bus to construct their models. However, the use of publicly available sensors is known to reduce the accuracy of detection models, while incorporating vendor-specific sensors into the dataset increases accuracy. To address the limitations of existing approaches, we present a reliable DBD system based on Graph Convolutional Long Short-Term Memory Networks (GConvLSTM) that enhances the precision and practicality of DBD models using public sensors. Additionally, we incorporate non-public sensors to evaluate the model's effectiveness. Our proposed model achieved a high accuracy of 97.5\% for public sensors and an average accuracy of 98.1\% for non-public sensors, indicating its consistency and accuracy in both settings. To enable local driver behavior analysis, we deployed our DBD system on a Raspberry Pi at the network edge, with drivers able to access daily driving condition reports, sensor data, and prediction results through a monitoring dashboard. Furthermore, the dashboard issues voice warnings to alert drivers of hazardous driving conditions. Our findings demonstrate that the proposed system can effectively detect hazardous and unsafe driving behavior, with potential applications in improving road safety and reducing the number of accidents caused by driver errors.

State-of-the-art survey of in-vehicle protocols and automotive Ethernet security and vulnerabilities.

With the help of advanced technology, the automotive industry is in continuous evolution. Modern vehicles are not only comprised of mechanical components but also contain highly complex electronic devices and connections to the outside world. Today's vehicle usually has between 30 and 70 ECUs (Electronic Control Units), which communicate with each other over standard communication protocols. There are different types of in-vehicle network protocols and bus systems, including the Controlled Area Network (CAN), Local Interconnected Network (LIN), FlexRay, Media Oriented System Transport (MOST), and Automotive Ethernet (AE). Modern cars are also able to communicate with other devices through wired or wireless interfaces such as USB, Bluetooth, Wi-Fi or even 5G. Such interfaces may expose the internal network to the outside world and can be seen as entry points for cyber-attacks. In this paper, the main interest is in the AE network protocol. AE is a special Ethernet design that provides the bandwidth needed for today's applications, and the potential for even greater performance in the future. However, AE is a "best effort" protocol, which cannot be considered reliable. This implies that it is not trustworthy in terms of reliability and timely deliveries. The focus of this paper is to present a state-of-the-art survey of security threats and protection mechanisms relating to AE. After introducing and comparing the different protocols being used in the embedded networks of current vehicles, we analyze the potential threats targeting the AE network and describe how attackers' opportunities can be enhanced by the new communication abilities of modern cars. Finally, we present and compare the AE security solutions currently being devised to address these problems and propose some recommendations and challenges to deal with security issue in AE protocol.

Interpretation of the electric vehicle operating point in real time

Abstract In this article, the performance parameters of the electric vehicle were investigated, and its operating point was defined using the core components (Battery, Inverter, and Motor). The test vehicle 2023 Cadillac Lyriq, provided by General Motors Inc., was driven on specified road segments, and the real-time data were retrieved using the integrated controller area network architecture. The neoVI–Fire 2 tool was connected to the vehicle system, which records the dynamic data, and Vehicle Spy software was used to convert the data into a readable format. Finally, the vector electric vehicle operating point was proposed, and the corresponding behavior was interpreted. This methodology could assist researchers in understanding the dynamic behavior of electric vehicle parameters to develop integrated techniques which augment the performance in real time.

A Novel Hybrid Quantum-Classical Framework for an In-Vehicle Controller Area Network Intrusion Detection

A Novel Hybrid Quantum-Classical Framework for an In-Vehicle Controller Area Network Intrusion Detection

Transfer Learning-Based Intrusion Detection System for a Controller Area Network

Transfer Learning-Based Intrusion Detection System for a Controller Area Network

Dynamic event-triggered practical stabilization of random suspension system based on immersion and invariance

This article investigates the practical stabilization problem of random quarter-car active suspension systems. An adaptive dynamic event-trigger strategy is proposed to stabilize the states of vehicle suspension in response to system uncertainty and controller area network resource constraints. Moreover, the model of random active suspension systems is extended to the general random robot systems; the controller is developed with the aid of a double dynamic surface filter, immersion and invariance (I&I) techniques, and event-triggered mechanisms. The results show that the semi-global stability of error systems is achieved, and there are some improvements in triggering times and adaptive estimation performance under the control framework. Finally, simulation comparison results are provided to prove the advantages of the proposed scheme.

DEVELOPMENT OF BATTERY MANAGEMENT SYSTEM AND REVIEW OF CANBUS DATA TRAFFIC

Recently, the use and production of electric vehicles has been increasing. In parallel with this increase, electric vehicle technologies are constantly developing. With the increase in the use of electric vehicles, the importance of BYS (Battery Management System), which controls the charge and discharge cycles of batteries, is increasing and researches are being carried out to increase their efficiency. Batteries with high energy density used in electric vehicles need to be kept under constant control. A large number of cells are used to obtain the high voltage and capacity required by electric vehicles. Each cell needs a battery management system to keep it under control. CAN (Controller Area Network) is used in BYS communication with the vehicle. In this study, the data traffic on the CAN line of the BYS used in today's electric vehicles has been analyzed. In this analysis, instant data of the battery pack is sent over the CAN line. The system includes BYS, charger and VCU (Vehicle Control Unit). An experimental setup with BYS, VCU and charger nodes was prepared and line load calculation was made on this experimental setup. Three different methods were used in the line load calculation process and the relationship of these methods with each other was compared. Different CAN speeds were used in the test stages and line load analysis was performed by changing the message sending frequencies. According to the data obtained from the prepared experimental setup, it was seen that the load of the messages on the CAN line was between 1.97% and 19.58%.

CarTwin—Development of a Digital Twin for a Real-World In-Vehicle CAN Network

Digital twins are used to replicate the behavior of physical systems, and in-vehicle networks can greatly benefit from this technology. This is mainly because in-vehicle networks circulate large amounts of data coming from various sources such as wired, or in some cases even wireless, sensors that are fused by actuators responsible for safety-critical tasks that require careful testing. In this work, we build a laboratory in-vehicle network that mimics a real vehicle network in regards to wire length, number of stubs and devices that are connected to it. The Controller Area Network (CAN), which is still the most popular communication bus inside cars, is used as a network layer. Using models defined in MATLAB for various subsystems, e.g., Anti-lock Braking System (ABS), Powertrain and Electric Power-Steering, deployed on automotive-grade microcontrollers, we evaluate the in-vehicle bus digital twin by providing realistic inputs and recording and reproducing in-vehicle network traffic. The experimental results showed good correlation between the output of the implemented digital twin and the data collected from an actual car.

New energy vehicle engine speed control method based on vehicle networking technology

In view of the current new energy vehicles only implement torque control on the drive motor, resulting in the engine speed fluctuation, affecting the vehicle running stability and reducing the engine efficiency and energy saving effect, a new energy vehicle engine speed control method based on Internet of vehicles technology is proposed. The battery and operation data are obtained through the sensor of the new energy vehicle Internet of vehicles, and the data are controlled and transmitted by the controller area network. The overall external dynamic characteristics of the engine are analyzed by using the average value model. According to the principle of engine dynamic system, the throttle intake manifold model, combustion torque model and power output sub model are constructed respectively. The speed is controlled by double working points According to the variable parameter PID control of the engine, the load prediction is introduced to optimize the speed control, and the internal pressure value of the accumulator is obtained through the SOP value to realize the speed control of the new energy vehicle engine. The experimental results show that the engine speed control method can effectively improve the energy-saving effect and efficiency of the engine, reduce the engine speed fluctuation, and improve the vehicle running stability under the condition of meeting the driver’s torque demand.

Congestion Intrusion Detection-Based Method for Controller Area Network Bus: A Case for KIA SOUL Vehicle

In the vehicle industry, connectivity and autonomy are becoming increasingly important features. One of the most used protocols for in-vehicle communication is the Controller Area Network (CAN) bus which manages the communication between networked components. However, the CAN bus, despite its critical importance, lacks sufficient security features to protect its network as well as the overall car system. Thus, vehicle network security is becoming increasingly crucial. Methods of intrusion detection help to improve the security of the in-vehicle network. This work aims to provide a model that enables effective detection of attacks such as fuzzy, DoS, and impersonation using the Deep Feedforward Neural Network (DeepFNN) model as well as the Long Short-Term Memory model. Moreover, the LSTM model presents the most satisfying outcome in terms of precision and recall metrics.

A comparative study of hash algorithms with the prospect of developing a CAN bus authentication technique

In this paper, the performances of SHA-3 final round candidates along with new versions of other hash algorithms are analyzed and compared. An ARM-Cortex A9 microcontroller and a Spartan -3 FPGA circuit are involved in the study, with emphasis placed on the number of cycles and the authentication speed. These hash functions are implemented and tested resulting in a set of ranked algorithms in terms of the specified metrics. Taking into account the performances of the most efficient algorithms and the proposed hardware platform components, an authentication technique can be developed as a possible solution to the limitations and weaknesses of automotive CAN (Controlled Area Network) bus – based embedded systems in terms of security, privacy and integrity. From there, the main elements of such a potential structure are set forth.

Highly Accurate Clock Synchronization With Drift Correction for the Controller Area Network

Modern vehicles, that have to be considered as safety-critical cyber-physical systems, require highly accurate <i>clock synchronization</i> (CS) among their distributed computing devices. Since Controller Area Network (CAN) is the predominant in-vehicle communication bus, it is highly relevant to support CS for CAN. This article proposes an original CS method for distributed in-vehicle networks based on CAN with both <i>offset</i> and <i>drift correction</i> . While offset correction is performed based on timestamps in periodic reference messages (RMs), our new method benefits from the re-synchronization mechanism of the CAN bit timing to apply highly accurate drift correction. Our algorithm does not make any modifications to the CAN protocol but requires the measurement of the phase error from the CAN controller. We derive analytical bounds for the expected clock differences and further validate the practicability of the proposed method by comprehensive experiments. As the main result, our method achieves a clock accuracy below <inline-formula><tex-math notation="LaTeX">$2\;\mu$</tex-math></inline-formula> s independent of important parameters such as the bit rate, RM period, bus utilization and time-varying clock drifts.

Modular Measurement System for System-Efficient ESD Design on System and Component Level

For the electrostatic discharge (ESD) characterization of components or systems, complex measurements of voltages and currents are required. The measurements are then used to characterize, model, and verify simulations. Both physical damage and upset are expected; however, performing these experiments on evaluation boards is often not suitable since the required current and voltage ports are not integrated. Furthermore, repeatedly reworking the integrated circuit (IC) will quickly destroy the evaluation board. A new modular experimental platform was therefore developed to flexibly perform such complex measurements. The modularity greatly simplifies measurements and allows for replacement of damaged components. The system consists of injection modules, such as universal serial bus (USB) and sub miniature version A (SMA) connections, excitation modules, such as a transmission line pulse and an ESD generator, modules for voltage and current measurements for characterization of transient voltage suppressor diodes and ICs, modules carrying passive channels, and active modules for the ICs being characterized. As active parts, which serve as device under tests, a set of USB 3.1 repeaters, controller area network (CAN) bus, and radio frequency (RF) front-end ICs are added to individual modules. The modules themselves, their characterization, and usage are explained in this article.

A Novel Gate-Controlled Dual Direction SCR With Enhanced Failure Current for On-Chip ESD Protection of Industry-Level Controller Area Network Bus

The working environment of the industry-level high-voltage communication bus is harsh. Strong electrostatic interference has become one of the key factors affecting the stability of the core module. However, the traditional silicon-controlled rectifier (SCR) is difficult to meet the electrostatic discharge (ESD) design requirements for high-voltage applications. Therefore, this article proposes a novel enhanced gate-controlled dual-direction SCR (EGC-DDSCR) with a high failure current, which can effectively ensure the ESD reliability of the controller area network (CAN) bus. By comparing the ESD performance of EGC-DDSCR, traditional gate-controlled DDSCR (GC-DDSCR), and double-GC-DDSCR (DGC-DDSCR), the physical behavior of the gate control mechanism is explored. Three types of SCR are designed based on a 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> BCD process. According to the physical principles of devices, 2-D electrical simulation and transmission line pulse (TLP) test results predict and verify the ESD parameters of the SCRs. The results show that EGC-DDSCR ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.64~\Omega$ </tex-math></inline-formula> ) has a smaller ON-resistance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> ) and higher robustness than GC-DDSCR ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.98~\Omega$ </tex-math></inline-formula> ) and DGC-DDSCR ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.90~\Omega$ </tex-math></inline-formula> ). In addition, the trigger voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{t1}$ </tex-math></inline-formula> :42.7 V), holding voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{h}$ </tex-math></inline-formula> :31.0 V), and failure current ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{t2}$ </tex-math></inline-formula> :16.7 A) of the device fully meet the ESD window of the target chip. EGC-DDSCR can be stably applied to CANL and CANH interfaces for on- chip bidirectional ESD protection.

Quantifying fuel-saving benefit of low-rolling-resistance tyres from heavy goods vehicle in-service operations

It is important to understand real-world performance of fuel-saving technologies for heavy goods vehicles (HGVs). This paper concerns applying data mining to quantify the fuel-saving benefit of a low-rolling-resistance (LRR) tyre using in-service operational data. Two HGVs of the same specification were employed, with one using LRR tyres and the other conventional tyres. A smartphone-based data logger was developed to collect data from the HGVs’ operations on publics roads for three months. Data from vehicle Controller Area Network, smartphone sensors, weather and elevation databases were collected. A data mining methodology was developed to mine data segments representing dynamically similar driving conditions between the two HGVs, and compare their fuel consumption rates through fitting data to a theoretical vehicle fuel consumption model. It was found that at a 95% confidence level the LRR tyres exclusively brought about a fuel-saving benefit between 6.89% and 8.37% in typical UK motorway driving conditions.

Classification of Normal and Malicious Traffic Based on an Ensemble of Machine Learning for a Vehicle CAN-Network

Connectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks, as it is lacking security mechanisms by design. It is crucial to design intrusion detection systems (IDS) with high accuracy to detect attacks on the CAN bus. In this paper, we design an effective machine learning-based IDS scheme for binary classification that utilizes eight supervised ML algorithms, along with ensemble classifiers. The scheme achieved a higher effectiveness score in detecting normal and abnormal activities when trained with normal and malicious CAN traffic datasets. Random Forest, Decision Tree, and Xtreme Gradient Boosting classifiers provided the most accurate results. Then we evaluated three ensemble methods, voting, stacking, and bagging, for this classification task. The ensemble classifiers achieved better accuracy than the individual models, since ensemble learning strategies have superior performance through a combination of multiple learning mechanisms. These mechanisms have a varied range of capabilities that improve the prediction reliability while lowering the possibility of classification errors. Our model outperformed the most recent study that used the same dataset, with an accuracy of 0.984.

Mitigating voltage fingerprint spoofing attacks on the controller area network bus

The Controller Area Network (CAN) bus suffers security vulnerabilities that allow message spoofing and masquerading Electronic Control Units (ECUs). A popular provision for mitigating these vulnerabilities is through the use of machine learning (ML) to derive ECU fingerprints based on the physical properties of bus signals. Particularly, voltage-based intrusion detection systems associate the message transmitter with its voltage fingerprint to detect conflicting logical ECU identifiers in the presence of cyberattacks. However, the signal characteristics depend on the operating conditions and hence the fingerprints need to be adapted overtime by online training of the underlying ML model. An adversary may exploit such a shortcoming to superimpose training data based on its own transmissions and thus bypass the protection mechanism. Such an attack not only allows device impersonation but also leads to rejecting transmissions of a legitimate ECU. This paper proposes an effective approach to thwart these attack scenarios. Our approach introduces unpredictably-scheduled transmissions involving one or multiple ECUs to confuse the adversary and ensure the generation of a legitimate fingerprinting dataset for online training. We validate the robustness of our approach using data collected from a real vehicle and show that it outperforms a prominent competing scheme by over 30% in terms of identifying malicious ECUs when the attacker could overwrite 50% of the retraining transmissions.