Controller Area Network Bus Research Articles

Design and Testing of Electric Drive System for Maize Precision Seeder

To improve the expandability, seeding accuracy, and operating speed range of the electric drive system (EDS) of precision seeders, this study constructed an EDS based on a controller area network (CAN) bus and designed a motor controller based on a field-orientated control (FOC) algorithm. Full-factorial bench and field tests based on seed spacing (0.1, 0.2, and 0.3 m) and operating speed (3, 6, 9, 12, and 15 km/h) were carried out to evaluate the performance of the EDS. The results of bench tests showed that seeding quality varied inversely with operating speed and positively with seed spacing. The average quality of feed index (QFI) at 0.1, 0.2, and 0.3 m seed spacing in bench tests was 88.38%, 96.67%, and 97.36%, with the average coefficient of variation (CV) being 20.13%, 16.27%, and 13.20%. Analysis of variance confirmed that both operating speed and seed spacing had a significant effect on QFI and CV (p < 0.001). The analysis of motor rotational speed accuracy showed that the relative error of motor rotational speed above 410 rpm did not exceed 2.24%, and the relative error had less influence on the seeding quality. The average QFI was 85.93%, 95.91%, and 96.24%, with the average CV being 21.12%, 15.50%, and 16.49% at 0.1, 0.2, and 0.3 m seed spacing in field tests. The methods and results of this study can provide a reference for the design and optimization of the EDS in a maize precision seeder and provide an effective solution for the improvement of maize yields.

CANival: A multimodal approach to intrusion detection on the vehicle CAN bus

Vehicles of today are composed of over 100 electronic embedded devices known as Electronic Control Units (ECU), each of which controls a different component of the vehicle and communicates via the Controller Area Network (CAN) bus. However, unlike other network protocols, the CAN bus communication protocol lacks security features, which is a growing concern as more vehicles become connected to the Internet. To enable the detection of intrusions on the CAN bus, numerous intrusion detection systems (IDS) have been proposed. Although some are able to achieve high accuracy in detecting specific attacks, no IDS has been able to accurately detect all types of attacks against the CAN bus. To overcome the aforementioned issues, we propose a multimodal analysis framework named CANival, which consists of time interval-based and signal-based analyzers developed by designing a novel Time Interval Likelihood (TIL) model and optimizing an existing model CANet. Experimental results show that our multimodal IDS outperforms the base models and enhances the detection performance testing on two recent datasets, X-CANIDS Dataset and SynCAN, achieving average true positive rates of 0.960 and 0.912, and true negative rates of 0.997 and 0.996, respectively.

CAN Bus: The Future of Additive Manufacturing (3D Printing)

Additive Manufacturing (AM) is gaining renewed popularity and attention due to low-cost fabrication systems proliferating the market. Current communication protocols used in AM limit the connection flexibility between the control board and peripherals; they are often complex in their wiring and thus restrict their avenue of expansion. Thus, the Controller Area Network (CAN) bus is an attractive pathway for inter-hardware connections due to its innate quality. However, the combination of CAN and AM is not well explored and documented in existing literature. This article aims to provide examples of CAN bus applications in AM.

A robust multi-stage intrusion detection system for in-vehicle network security using hierarchical federated learning

As connected and autonomous vehicles proliferate, the Controller Area Network (CAN) bus has become the predominant communication standard for in-vehicle networks due to its speed and efficiency. However, the CAN bus lacks basic security measures such as authentication and encryption, making it highly vulnerable to cyberattacks. To ensure in-vehicle security, intrusion detection systems (IDSs) must detect seen attacks and provide a robust defense against new, unseen attacks while remaining lightweight for practical deployment. Previous work has relied solely on the CAN ID feature or has used traditional machine learning (ML) approaches with manual feature extraction. These approaches overlook other exploitable features, making it challenging to adapt to new unseen attack variants and compromising security. This paper introduces a cutting-edge, novel, lightweight, in-vehicle, IDS-leveraging, deep learning (DL) algorithm to address these limitations. The proposed IDS employs a multi-stage approach: an artificial neural network (ANN) in the first stage to detect seen attacks, and a Long Short-Term Memory (LSTM) autoencoder in the second stage to detect new, unseen attacks. To understand and analyze diverse driving behaviors, update the model with the latest attack patterns, and preserve data privacy, we propose a theoretical framework to deploy our IDS in a hierarchical federated learning (H-FL) environment. Experimental results demonstrate that our IDS achieves an F1-score exceeding 0.99 for seen attacks and exceeding 0.95 for novel attacks, with a detection rate of 99.99%. Additionally, the false alarm rate (FAR) is exceptionally low at 0.016%, minimizing false alarms. Despite using DL algorithms known for their effectiveness in identifying sophisticated and zero-day attacks, the IDS remains lightweight, ensuring its feasibility for real-world deployment. This makes our model robust against seen and unseen attacks.

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.

CANSat-IDS: An adaptive distributed Intrusion Detection System for satellites, based on combined classification of CAN traffic

The increasing dependence on satellite technology for critical applications, such as telecommunications, Earth observation, and navigation, underscores the need for robust security measures to safeguard these assets from potential cyber threats. Moreover, as many satellite systems rely on the Controller Area Network (CAN) protocol for efficient data exchange among onboard subsystems, they become prime targets for cyberattacks. While contributions present various options for detecting attacks in the CAN bus, no one proposes an architecture suitable for satellite systems. To address this concern, this paper presents a novel approach to develop an adaptive distributed Intrusion Detection System (IDS) for satellites, which integrates machine and deep learning techniques for the classification of CAN frames. This system is specifically designed to overcome the inherent power and computational challenges of satellite operations by executing time-based anomaly detection on board, and content-based detection at the ground segment. To evaluate the effectiveness of the proposed solution, experiments are conducted using representative Datasets. The obtained results demonstrate that the distributed IDS presented in this research offers a promising solution to improve the security of satellite systems by achieving high detection rates ranging from 91.12% to 99.86% (F1-score).

Application of optimizing advanced encryption standard encryption algorithm in secure communication of vehicle controller area network bus

Introduction: As the main means of information exchange within vehicles, the safety of the controller area network bus directly affects the safe operation of the vehicle and the safety of passengers’ lives and property.Methods: To enhance its secure communication function, this study utilizes advanced encryption standard algorithms and improves the S-box of the algorithm to solve problems such as extended processing time. A secure communication system for the local area network bus of the vehicle controller is designed based on optimized advanced encryption standard algorithms.Results and Discussion: The results showed that when the file size was 200MB, the encryption and decryption time spent by the research method was 469.8 s and 528.5 s, respectively, which are significantly lower than traditional methods. In the simulation results, under both non-encrypted and encrypted transmission, the information remained intact throughout the entire transmission process. This indicated that the optimization algorithm effectively reduced encryption processing time and system resource consumption while ensuring data confidentiality and integrity. The new system meets the security requirements of the local area network bus of vehicle-mounted controllers.Conclusion: This study not only enhances the security of in-vehicle networks but also promotes the application and development of related encryption technologies in the field of vehicle networking. It provides strong technical support for the further development of vehicle networking and the safe operation of intelligent vehicles.

Voltage inspector: Sender identification for in-vehicle CAN bus using voltage slice

Controller Area Network (CAN) serves as the neural system of modern cars, connecting and coordinating various electronic control units (ECUs) responsible for vehicle operation. However, the inherent features of CAN, such as broadcast communication and lack of authentication, make it increasingly vulnerable to cyberattacks. Although existing intrusion detection systems (IDSs) perform well in detecting malicious attacks, they often lack the ability to accurately locate the senders of these malicious messages. In this paper, we propose an efficient sender identification method called Voltage Inspector, which leverages physical voltage signal slice to accurately identify the source of messages for CAN bus. We start by extracting voltage slices from the raw physical signals of the CAN bus. Next, we leverage clustering technology to infer the ECU mapping information, which is typically considered confidential. This mapping information, combined with a machine learning classifier, is then utilized to construct an identification model capable of accurately identifying the sender of each message. To validate the effectiveness of our proposed method, we conducted extensive experiments using a publicly available voltage dataset collected from ten real vehicles. The experimental results demonstrate the remarkable accuracy of our approach, achieving a minimum identification accuracy of 99%. Furthermore, our method significantly reduces the data volume by half and reduces the identification time by a quarter when compared to state-of-the-art methods. Our research reveals that even a small portion of the voltage signal can be used to uniquely fingerprint an ECU. We emphasize that our method serves as an alternative identification approach and can complement existing works in the field.

IDS-DEC: A novel intrusion detection for CAN bus traffic based on deep embedded clustering

As the automotive industry advances towards greater automation, the proliferation of electronic control units (ECUs) has led to a substantial increase in the connectivity of in-vehicle networks with the external environment. However, the widely used Controller Area Network (CAN), which serves as the standard for in-vehicle networks, lacks robust security features, such as authentication or encrypted information transmission. This poses a significant challenge to the security of these networks. Despite the availability of powerful intrusion detection methods based on machine learning and deep learning, there are notable limitations in terms of stability and accuracy in the absence of a supervised learning process with labeled data. To address this issue, this paper introduces a novel in-vehicle intrusion detection system, termed IDS-DEC. This system combines a spatiotemporal self-coder employing LSTM and CNN (LCAE) with an entropy-based deep embedding clustering. Specifically, our approach involves encoding in-vehicle network traffic into windowed messages using a stream builder, designed to adapt to high-frequency traffic. These messages are then fed into the LCAE to extract a low-dimensional nonlinear spatiotemporal mapping from the initially high-dimensional data. The resulting low-dimensional mapping is subjected to a dual constraint in conjunction with our entropy-based pure deep embedding clustering module. This creates a bidirectional learning objective, addressing the optimization problem and facilitating an end-to-end training pattern for our model to adapt to diverse attack environments. The effectiveness of IDS-DEC is validated using both the benchmark Car Hacking dataset and the Car Hacking-Attack & Defense Challenge dataset. Experimental results demonstrate the model's high detection accuracy across various attacks, stabilizing at approximately 99% accuracy with a 0.5% false alarm rate. The F1 score also stabilizes at around 99%. In comparison with unsupervised methods based on deep stream clustering, LSTM-based self-encoder, and classification-based methods, IDS-DEC exhibits significant improvements across all performance metrics.

Greener view, safer drive: Using repeated field experiments and deep transfer learning technique to investigate impacts of urban road landscapes on driving performance

Driving in urban environments is an essential part of urban residents’ daily life. We still know little about impacts of a wide range of greenness on driving performance in real urban environments, after controlling for socioeconomic, demographic, driving record, and other environmental factors. This missing knowledge prevents policymakers and professionals from using appropriate planning and design of green landscapes to create safe driving environments for numerous urban residents. This study aimed to address this significant knowledge gap by using real-world driving experiments. Each of thirty-four residents performed seven driving tasks so 238 driving tasks were completed in total. Each task lasted one hour and followed a randomly assigned sequence. Road greenness and other environmental characteristics were analyzed using deep transfer learning semantic segmentation based on live videos (30 frames per second), recorded by a camera positioned to capture the driver’s eye view. A serial communication technology, known as Controller Area Network bus (CANbus), was employed to continuously measure driving performance using four parameters. A series of hierarchical regression analyses yielded three major findings: First, an increased mean of greenness was associated with improved driving performance, as demonstrated by all four parameters. Second, an increased variation of greenness was also associated with better driving performance in three parameters. Finally, the mean of greenness displayed a stronger positive relationship with driving performance than the variation of greenness in three parameters. The findings imply that both the quantity and quality of green landscapes are critical for promoting driving performance in urban areas.

Design and Fabrication of an Autonomous Rice Transplanter

The goal of this study was to create a prototype of an automatically guided rice transplanter using a Pixhauk flight controller, 3DR telemetry, Raspberry Pi, Tof sensor, gyro sensor, and other GPS navigation sensors to determine the field performance of that machine. GPS was used to obtain data such as location, direction, and speed, whereas machine vision can provide a navigation line. The next position of the transplanter can be determined using feature points extracted from the navigation line. The controller area network bus complies with the actuator control command and data communication protocols. Electrical actuators control steering and transmission based on a vehicle's location in a field. The percentage of missing, buried, and floating hills was observed to be 1.6%, 1.4%, and 3.0%, respectively, as the maximum values were found in clay soil at T1. Over time, percentages of losses decreased, and the lowest percentage of losses was given. The lowest percentage of missing, buried, and floating hills was found at 0.8%, 0.2%, and 0.8%, respectively, in clay loam soil at T5. Planting efficiency improved, and losses decreased as treatment levels upgraded. Clay loam soil in T5 had the highest planting efficiency percentage (88%), whereas clay soil in T1 had the lowest (61%) proportion. The findings indicated that T4 and T5 enhanced rice output by decreasing the losses incurred during seedling transplantation. The study suggested that the developed rice transplanter might be suitable for planting rice seedlings to establish meaningful mechanization through precision agriculture technology.

How Do We Calibrate a Battery Electric Vehicle Model Based on Controller Area Network Bus Data?

Transforming an up-to-date vehicle into a measurement system is a rewarding task due to the large number of different sensors in the onboard control and diagnostic systems. These procedures are not performed by a single control unit; it is necessary to share the signal values over a communication network, to which an external device can be connected to record the real traffic. The paper aims to use these recorded data for 1 DOF longitudinal vehicle and powertrain model validation. For repeatability, three city routes are selected: plain road, smaller road grade, and higher road grade in both directions. Therefore, the drivetrain system is tested in a high load range, even with long-term recuperation. The altitude changes are recorded with a DGPS system. By the recorded measurements, the vehicle and the drivetrain model can be calibrated, such as the air drag parameters, the rolling resistances, and the efficiencies of the drivetrain. The validation criteria are defined for speed tracking, and the relative tolerance of the cumulated energy should be below 10%. At the end of the day, a developed model is ready for energetic analysis or control strategy design. The energy balance of the applied cycles is also presented to prove that.

Engineering Autonomous Mobility: Wired and Wireless CAN Bus Design Principles

<div>The controller area network (CAN) bus, the prevailing standard for in-vehicle networking (IVN), has been used for more than four decades, despite its simple architecture, to establish communications between electronic control units (ECUs). Weight, maintenance overheads, improved flexibility, and wiring complexity escalate as the quantity of ECUs rises, especially for high-demand autonomous vehicles (AVs). The primary objective of this study is to examine and discuss the significant challenges that arise during the migration from a wired CAN to a wireless CAN (WCAN). Suggested remedies include changing the configuration of the conventional ECU, creating a hidden wireless communication domain for each AV, and developing a plan to counteract the jamming signals. The simulation of the proposed WCAN was done using MATLAB and validated using OPNET analysis. The results showed that the packet loss of the eavesdropping electronic control unit ranged from 63% to 100%. Anti-jamming results show that when packet loss reaches 2% for a continuous period of time of 0.01 sec, the passive channel is automatically activated, ensuring secure data transmission.</div>

A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10−3, 3.03 × 10−3, and 30.66 × 10−3, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction.

Development of an Improved Communication Control System for ATV Electric Vehicles Using MRS Developers Studio

Transmission, energy management, and distribution systems are critical components of modern electric vehicles, encompassing all sectors of the power system through communication control technology. One widely used communication system in electric vehicles is the Controller Area Network (CAN). This research aims to investigate the development of CAN BUS technology, adapted from large trucks, to control the communication system within an ATV electric vehicle using a communication format similar to bus Communication. The communication control system includes several components: the engine switch, headlight, turn signal, emergency light, horn, forward/reverse gear, and accelerator. The system’s communication protocols were developed using MRS Developers Studio version 1.40 software to create the data transmission and reception formats for the vehicle’s components. The communication system employs three PLC 1.033.30B.00 type E control boxes, each with limited analog and digital input/output ports. The sequence of communication control begins with the engine start/stop operation, as the system will not function unless the engine is started first. The headlight operation is processed within the CAN BUS1 control box. Simultaneously, the turn signal and emergency light functions are controlled by CAN BUS1 and displayed on both the CAN BUS2 (front of the vehicle) and CAN BUS3 (rear of the vehicle) control boxes. Additionally, the accelerator function is managed within the CAN BUS2 control box and displayed on the CAN BUS3 control box. However, this operation is contingent upon the forward/reverse gear selection, managed by CAN BUS1 and processed by CAN BUS3. All system operations are designed within the software’s programming paths. The communication system operates using CAN-High and CAN-Low lines, and communication data fields can be monitored using the PCAN-View software version 4.2.1.533. This study demonstrates the feasibility and effectiveness of adapting CAN BUS technology for ATV electric vehicles, providing insights into the integration and control of various vehicular components within a unified communication framework.

Multi-classification in-vehicle intrusion detection system using packet- and sequence-level characteristics from time-embedded transformer with autoencoder

In recent years, the increased application of controller area network (CAN) protocols has made it the de facto standard for communication between electronic control units (ECUs) in the automotive and transportation fields. This widely used protocol was designed as a reliable and straightforward broadcast-based protocol that connects ECUs without considering security concerns such as node authentication or traffic encryption. Despite its efficiency, this tradeoff makes the CAN bus vulnerable to attacks. Implementing intrusion detection systems (IDSs) based on machine learning (ML) can address these security challenges effectively. However, existing ML-based IDSs have limited classification capabilities, lack adaptability and time sensitivity, incomprehensive analysis, and produce high false-negative rates (FNR), while attack schemes are becoming increasingly complex, resulting in insufficient capability of intrusion detection in real-time and insufficient ability to offer reliable protection. Therefore, our study proposes a novel in-vehicle IDS for multiclass classification using both packet- and sequence-level characteristics extracted from an autoencoder and a variant transformer (Time-embedded Transformer) with an improved position encoding mechanism, which analyses CAN traffic in-depth from various perspectives to overcome the existing challenges above. Both standard (Car-Hacking) and advanced (ROAD) datasets are used to evaluate the capabilities of our proposed IDS. The evaluation results demonstrated 100 % detection accuracy and 0 % FNR for both the Car-Hacking and ROAD Masquerade datasets, which also peaked at the highest F1 score for the ROAD Fabrication dataset, emphasizing superior intrusion detection to minimize FNR of the proposed model with high adaptability through its multi-dimensional analysis at packet- and sequence-level.

Demonstration of denial of charging attack on electric vehicle charging infrastructure and its consequences

The recent upsurge in electric vehicle (EV) adoption has led to greener mobility but has also broadened the attack surface due to the increased interconnection between the entities like EV, EV charger, grid etc. We show in this paper that among these entities, the EV charger provides a possible attack surface through the available communication network. Adversaries at a minimum can disrupt the vehicular charging process known as denial of charging (DoC) attack. This attack is demonstrated on the real hardware setup of an EV charging, where we have considered the Bharat EV DC charging standard (BEVC-DC001) adopted by India which uses the controller area network (CAN) bus to communicate between EV charger and EV. The DoC attack can have significant consequences both on the electrical grid as well as individuals. The EV chargers (with connected EV) collectively serve as a large load demand, whose sudden inaccessibility would disrupt the supply–demand balance, triggering over frequency relays to either cause local or national blackout. Such a scenario is presented in this work on a microgrid (MG), in a real-time OPAL-RT environment. Not only can this attack lead to major transportation related problems but would also disrupt medical and emergency services.

CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus

Considering the complexity of network traffic in IoV operations, methods that can identify complex patterns become useful. Machine learning fosters several techniques to enhance the detection, prevention, and mitigation of cyberattacks. However, important features are not addressed in the current state-of-the-art security datasets for IoV. For example, in the case of intra-vehicle communications, it is critical to consider the interaction among multiple Electronic Control Units (ECUs). Also, mimicking a realistic IoV environment is not simple since establishing a test environment requires considerable financial investment. Hence, there is a need for a testbed composed of several real ECUs in an IoV environment comprising network traffic. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to foster the development of new cybersecurity solutions for IoV operations. To accomplish this, five attacks were executed against the fully intact inner structure of a 2019 Ford car, complete with all ECUs. However, the vehicle was immobile and incapable of causing any potential harm or injuries. Hence, all attacks were carried out on the vehicle without endangering the car’s driver or passengers. These attacks are classified as spoofing and Denial-of-Service (Dos) and were carried out through the Controller Area Network (CAN) protocol. This effort establishes a baseline complementary to existing contributions and supports researchers in proposing new IoV solutions to strengthen overall security using different techniques (e.g., Machine Learning — ML). The CICIoV2024 dataset has been published on CIC’s dataset page.

Performance Comparison of Timing-Based Anomaly Detectors for Controller Area Network: A Reproducible Study

This work presents an experimental evaluation of the detection performance of eight different algorithms for anomaly detection on the Controller Area Network (CAN) bus of modern vehicles based on the analysis of the timing or frequency of CAN messages. This work solves the current limitations of related scientific literature, which is based on a private dataset and lacks open implementations and a detailed description of the detection algorithms. These drawbacks prevent the reproducibility of published results, making it impossible to compare a novel proposal against related work, thus hindering the advancement of science. This article solves these issues by publicly releasing implementations and labeled datasets and by describing unbiased experimental comparisons.

A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks.

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats.