Engineering Autonomous Mobility: Wired and Wireless CAN Bus Design Principles

With the rapid development of autonomous vehicles (AVs), vehicle safety has attracted attention. Controller area network (CAN) bus without authentication and encryption mechanisms is a weakness of the in-vehicle network (IVN), and attackers always primarily target the CAN bus. Attacks on the AVs' CAN bus are more frequent because AVs have more external interfaces and sensors than ordinary vehicles. To address the abovementioned issues, this paper proposes a lightweight encryption and authentication scheme for the CAN bus of AVs using message authentication codes and Grain stream cipher. The scheme realizes efficient authentication between electronic control units and a counter re-synchronization protocol is used to ensure that authentication failure owing to counter inconsistency is prevented. Blom key management scheme is employed to rapidly distribute and update session keys, avoiding the complicated process of updating pairwise keys. In addition, considering the scalability of the IVN, a key distribution protocol for new nodes was provided. The security proof using Burrows-Abadi-Needham logic and security analysis prove that the proposed scheme can satisfy the security requirements of the IVN. In addition, performance analysis shows that the proposed scheme can meet real-time requirements and has little impact on the busload.

The Controller Area Network (CAN) bus system works inside connected cars as a central system for communication between electronic control units (ECUs). Despite its central importance, the CAN does not support an authentication mechanism, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways: denial of service, fuzzing, spoofing, etc. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We first inject attacks at the CAN bus system in a car that we have at our disposal to generate the attack dataset, which we use to test and train our model. Our results demonstrate that our classifier is efficient in detecting the CAN attacks. We achieved a detection accuracy of 99.9949%.

The modern car is increasingly connected. That connection is magnified by the presence of a large number of electronic control units (ECUs). The communication between the ECUs of a modern car is assured by the Controller Area Network (CAN) bus system. Despite its importance, the CAN bus system is bereft of security mechanisms making it vulnerable to numerous security attacks. When an attacker succeeds in compromising the ECUs, they can take control and stop the engine, disable the brakes, turn the lights on/off, etc. An intrusion detection system (IDS) can be deployed as an appropriate security measure to detect the malicious network traffic in the CAN bus system. In this paper, we propose a Convolutional Neural Network (CNN)-based network attacks IDS for protecting the CAN bus system. For efficiency reasons, we generated our own datasets from three car models. Our experiment results demonstrate that our classifier is efficient for detecting the CAN bus system attacks, and it performs with a high accuracy of 99.99% and a detection rate of 0.99.

In-vehicle networks (IVNs) connect numerous sensors, processors, and Electronic Control Units (ECUs) to enable real-time communication, but their growing complexity has amplified cybersecurity concerns. The Controller Area Network (CAN) bus, along with its variants CAN-FD, CAN-XL, and complementary protocols such as LIN and FlexRay, remains the backbone of IVNs. However, these protocols (the CAN specifically) were not originally designed to meet the stringent security demands of emerging autonomous and electric vehicles. This survey critically examines existing IVN security literature with a particular focus on intrusion detection systems (IDS) developed for CAN-based architectures. Unlike prior reviews that emphasize algorithmic design or attack taxonomy, this survey provides a comparison of IDS approaches (typically CAN-based due to its dominance in industry and research domain) based on physical-layer features, statistical parameters, information theory, and learning-based methods. We highlight unresolved security limitations of the CAN bus, synthesize mitigation strategies across industrial domains (automotive, aerospace, marine, agricultural, and battery systems), and identify overlooked challenges. Finally, we outline key research frontiers, such as hardware-assisted protection, edge intelligence, blockchainbased authentication, generative AI-driven anomaly detection, and quantum-safe cryptography—toward building resilient next-generation IVNs.

An in-vehicle controller area network (CAN) bus is vulnerable because of increased sharing among modern autonomous vehicles and the weak protocol design principle. Spoofing attacks on a CAN bus can be difficult to detect and have the potential to enable devastating attacks. To effectively identify spoofing attacks, we propose the authentication of sender identities using a recurrent neural network with long short-term memory units (RNN-LSTM) based on the features of a fingerprint signal. We also present a way to generate the analog fingerprint signals of electronic control units (ECUs) to train the proposed RNN-LSTM classifier. The proposed RNN-LSTM model is accelerated on embedded Field-Programmable Gate Arrays (FPGA) to allow for real-time detection despite high computational complexity. A comparison of experimental results with the latest studies demonstrates the capability of the proposed RNN-LSTM model and its potential as a solution to in-vehicle CAN bus security.

Controller Area Network (CAN) is an effective choice for the automotive industries due to its simplicity, low cost nature, decrease of wiring density and in addition to that it provide connectivity with multiple nodes with single wiring pair. This paper aims in describing an ARM 7 LPC 2129 based Temperature measurement system with Controller Area Network (CAN) bus. Objective of this paper is to construct a hardware proposal for communication between nodes with CAN bus. A Node to Node communication link has been established which are connected via CAN bus so as to observe and control the temperature values transferring on the CAN bus and send the command signals onto the another node. The hardware boards are designed to be capable of supporting high speed bit rate up to 1Mbps. The CAN communication module can transmit or receive data following CAN Protocol. With the help of the software used, the CAN-BUS communication would be clear to the nodes of the ARM 7 Controllers in the system. The software part is done in KEIL microvision-4 using Embedded -C.

Modern vehicles have significantly increased the number of Internet of Things (IoT) devices called electronic control units (ECUs) connected by in-vehicle networks to provide enhanced features and safety. These devices communicate with the environment and have brought the notion of the Internet of vehicles. The controller area network (CAN) bus facilitates efficient ECU communications and is the standard protocol used by every vehicle, but it is susceptible to remote attacks. Consequently, it is desirable to monitor the CAN bus for malicious activities, such as data injection attacks, that can compromise the vehicular operations. We present an anomaly detection technique that uses an extreme gradient boosting machine (GBM) learning algorithm to categorize unexpected occurrences in the CAN data payload. We further combine GBM with a ten-fold cross-validation method to improve prediction performance. Moreover, we use the early -stopping and grid search approaches to overcome overfitting without affecting model accuracy. We evaluate our detection approach on real CAN bus datasets collected from Hyundai Sonata, a KIA Soul, and Chevrolet Spark with different attack scenarios, such as Denial-of-Service (DoS), fuzzy, spoofing, and malfunction attacks. Using standard metrics, such as accuracy, recall, precision, F1 score, and false-positive rate, the performance analysis of the proposed model achieved an overall accuracy of over 99 percent.

There have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their Controller Area Network (CAN) bus. In this article, using injected speed reading and Revolutions Per Minute (RPM) reading messages in in-motion vehicle, we examine the ability of the Pearson correlation and the unsupervised learning methods k-means clustering and Hidden Markov Model (HMM) to differentiate ’no-attack’ and ’under-attack’ states of the given vehicle. We found that the Pearson correlation distinguishes the two states, the k-means clustering method has an acceptable accuracy but high false positive rate and HMM detects attacks with acceptable detection rate but has a high false positive in detecting attacks from speed readings when there is no attack. The accuracy of these unsupervised learning methods are comparable to the ones of the supervised learning methods used by CAN bus Intrusion Detection System (IDS) suppliers. In addition, the article shows that studying CAN anomaly detection techniques using off-vehicle test facilities may not properly evaluate the performance of the detection techniques. The results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the Electronic Control Units (ECUs) collaborate in building effective techniques for the detection of injection of fabricated message attacks.

This paper introduces the principle of controller area network (CAN) bus and the structure of fuel cell vehicle control system based on CAN bus. It focuses on electrical air -condition control system for fuel cell vehicle based on CAN bus, which consists of an air condition control node and an air condition compressor drive. The design of electrical air condition control system based on CAN bus is analyzed in detail, including hardware design and software design. The hardware design mainly includes a general-purpose microcontroller P89C51Rx2, a dedicated microcontroller ST72141 for brushless DC Motor and an independent CAN controller etc.. The running result of air-condition control system proves the flexibility and efficiency and reliability of the system design.

The controller-area network (CAN) bus has a wide range of applications. It plays a key role in information transfer. In fact, a failure of the CAN bus causes information transfer to fail. Therefore, fault diagnosis of the CAN bus is necessary. In addition, CAN bus failures generally result from sample imbalance problems. Therefore, this study proposes a CAN bus fault diagnosis (CANFD) method that integrates a Laplace network (LaplaceCNN) and uses a class-imbalance dataset. The method first utilises the adaptive synthetic (ADASYN) oversampling algorithm to process the class-imbalance CANFD dataset. LaplaceCNN is trained on the processed balanced dataset to diagnose CAN bus faults. To collect the frequency-domain characteristics and feed them into the deep network, LaplaceCNN uses Laplace wavelet convolution as the initial layer of the network. Multiscale wide convolutional and dense residual blocks are proposed to enhance the feature extraction capability of LaplaceCNN and stabilise the model performance. Introducing focal loss further improves the performance of LaplaceCNN on a class-imbalanced dataset. Experiments demonstrate that the proposed method performs better in terms of fault diagnosis.

The remanufacturing industry as a whole and the automotive sector in particular have, over the years, proven to be beneficial to the environment and economically lucrative to the companies involved as well as to their customers. However, remanufacturing is associated with complicating characteristics, not least to mention the process of core acquisition.The automotive industry is one of the earliest adapters of remanufacturing. Parts like engines, brake calipers and servo pumps are common targets for remanufacturing. Modern cars also have several embedded computers, often referred to as electronic control units that communicate, share information and verify each other over a Controller Area Network (CAN) bus. Due to their high value and an increasing trend in the amount of CAN bus mechatronic devices, interest in their remanufacture is growing.Previous research has shown that it is preferable that the remanufacturer is an original equipment manufacturer (OEM), or has a close relation to the OEM, in order to achieve a well-performing remanufacturing business. In the automotive industry, there are many small and medium-sized enterprises (SMEs) that perform remanufacturing; for these enterprises, the challenges to have a profitable business are even harder. This is because the OEMs will not release any information on the communication parameters and therefore will not support the independent remanufacturing business. As a consequence, the independent remanufacturers, often SMEs, have to perform substantial reverse engineering.This paper presents a qualitative research study, based on interviews at SMEs regarding challenges linked to the reverse logistics of SMEs remanufacturing and trading used automotive mechatronic devices, to identify specific challenges concerning the collection phase of automotive mechatronic remanufacturing. Challenges previously identified by researchers are confirmed, additional challenges within the collection phase are recognized, and challenges expected to arise when remanufacturing and trading automotive electronic CAN bus mechatronic devices are identified. The major concern for the involved companies when commencing future challenges is the handling, transportation and storing of cores. Even though the cores today mainly consist of mechanical devices, these challenges are still present; they are expected, however, to become even more crucial when cores contain a higher degree of mechatronic devices.

Smart vehicles and industrial control systems becoming increasingly complex. They are comprised of a large number of connected intelligence sensor devices. For such systems, Controller Area Network (CAN) bus offer high-integrity serial communication capabilities. It transformed the way how these systems are networked. Due to the lack of data security features on CAN-enabled systems, many of these systems are vulnerable to a wide range of cyber threats. This article proposed the development of a crypto-based subsystem that is capable of supporting CAN authenticated data encryption/decryption, crypto-provable data traceability, and replay attack detection capabilities. Data confidentiality was achieved via the deployment of a lightweight block cipher authenticated encryption scheme based on TinyJAMBU-128. Crypto-provable data traceability was accomplished through the utilization of a block-chaining approach. Meanwhile, an anti-replay attack mechanism that implements CAN message context awareness has been tested and validated under various data infection rates. Our CAN security subsystem was fully implemented and deployed on a testbed with multiple STM32 Nucleo development boards. System performance for our security schemes was analyzed and compared with traditional encryption schemes AES, ARIA, and Camellia with SHA-512 for supporting message authentication. Based on our performance results, the proposed security subsystem achieved the lowest CAN bus load and average message overhead compared to other encryption schemes. In the case of the anti-replay attack mechanism, we were able to reach a detection rate of 99.99% for data infection rate below 20%.

For a modern vehicle, if a sensor in the vehicle Anti-lock Braking System (ABS) or the controller area network (CAN) bus is attacked during a brake process, the vehicle will lose driving direction control and driver’s life will be highly threatened. However, current methods for detecting attacks are not sufficiently accurate and no methods can provide attack mitigation. To ensure vehicle ABS security, we propose an attack detection method to accurately detect both sensor attack and CAN bus attack in a vehicle ABS, and an attack mitigation strategy to mitigate their negative effects on the vehicle ABS. In our attack detection method, we build a vehicle state space equation which considers real-time road friction coefficient to predict vehicle states (i.e., wheel speed and longitudinal brake force) with their previous values. Based on sets of historical measured vehicle states, we develop a search algorithm to find out attack changes (vehicle state changes because of the attack) by minimizing errors between predicted vehicle states and measured vehicle states. In our attack mitigation strategy, attack changes are subtracted from measured vehicle states to generate correct vehicle states for the vehicle ABS. We conducted first real sensor attack experiments to show how a magnet affects sensor readings. Our experimental results demonstrate that our attack detection method can detect sensor attack and CAN bus attack more accurately compared with existing methods, and also our attack mitigation strategy almost eliminates attack’s effects on the vehicle ABS.

As connectivity between and within vehicles increases, so does concern about safety and security. Various automotive serial protocols are used inside vehicles such as Controller Area Network (CAN), Local Interconnect Network (LIN), and FlexRay. CAN Bus is the most used in-vehicle network protocol to support exchange of vehicle parameters between Electronic Control Units (ECUs). This protocol lacks security mechanisms by design and is therefore vulnerable to various attacks. Furthermore, connectivity of vehicles has made the CAN Bus vulnerable not only from within the vehicle but also from outside. With the rise of connected cars, more entry points and interfaces have been introduced on board vehicles, thereby also leading to a wider potential attack surface. Existing security mechanisms focus on the use of encryption, authentication, and vehicle Intrusion Detection Systems (IDS), which operate under various constraints such as low bandwidth, small frame size (e.g., in the CAN protocol), limited availability of computational resources, and real-time sensitivity. We survey and classify current cryptographic and IDS approaches and compare these approaches based on criteria such as real-time constraints, types of hardware used, changes in CAN Bus behaviour, types of attack mitigation, and software/ hardware used to validate these approaches. We conclude with mitigation strategies limitations and research challenges for the future.

Recent advancements in transportation technology have necessitated a seamless integration of sensors, cameras, and communication protocols for autonomous vehicles. Ensuring cybersecurity for these vehicles has become critical, as they are vulnerable to attacks that could jeopardize safety and performance. While machine learning and deep learning models could be developed to address cybersecurity issues in autonomous vehicles, developing these models requires large datasets for training. Obtaining real-world data from actual vehicles also presents significant challenges. To address these limitations, a low-cost Pro-CHASER testbed— Prototype testbed for Car Hacking Attack Simulation and Evaluation using Raspberry Pi—was developed using a Controller Area Network (CAN) bus network with CAN-HAT (Hardware Attached on Top). This testbed utilized CAN to simulate autonomous vehicle functions and explore attack vectors. Microsoft Threat Modelling Tool, and MITRE ATT&CK framework were used to identify the vulnerabilities in the context of autonomous vehicles. The identified vulnerabilities were exploited using the designed testbed by employing many security testing tools to simulate CAN message injection attacks, USB exploits, and ROS-based fake image publisher attack. Through these attack simulations, the testbed functionalities were successfully compromised. The results demonstrated that message injection into the CAN bus manipulated sensor data that led to erroneous decision-making. Unauthorized access to the physical access points led to the injection of a malicious payload via a USB port. The computer vision system’s response to the false data ultimately compromised the vehicle’s perception and control. The findings highlight the capability of Pro-CHASER in identifying cyber security risks in autonomous vehicles, which offers a practical and low-cost platform for threat simulation and evaluation.