Flow-based Aggregation of CAN Frames with Compressed Payload

Controller Area Network (CAN) is a bus standard commonly used in the automotive industry. A large number of researches have been carried out on the identity authentication and intrusion detection technology of the in-vehicle network for attacks. This paper presents a method to extract the voltage of CAN bus terminal to obtain the equipment fingerprint. The method identifies different ECUs by measuring the voltage difference on the vehicle network, selects features only from the time domain and detects based on a single frame. This paper mainly focuses on the influence of temperature on the fingerprint of CAN bus terminal. By using a high and low temperature test chamber to simulate different temperature environments during vehicle operation, a large number of CAN frames have been collected for fingerprint extraction. By studying the relationship between temperature and CAN bus terminal fingerprint, the influence of temperature on fingerprint has been compensated. After temperature linear compensation, the recognition rate of voltage-based CAN bus terminal fingerprint extraction increases from 71.2% to 87.6% at 0–80 °C.

This system is designed to test automotive CAN (Controller Area Network) controlled devices or entire network. This system not only can display CAN frames (CAN 2.0A/B) received from or sent onto CAN bus, but also can record data on log files for off-line evaluation. Users can configure several monitoring modes and CAN channel features of the system with PC application. Furthermore the USB technology adopted in the system make it a more practical and convenient CAN bus testing system.

<div class="section abstract"><div class="htmlview paragraph">The amount of data communicated in automotive networks is increasing rapidly, leading to more complex systems alongside the rising number of Electronic Control Units (ECUs) in modern vehicles. As the number of ECUs has risen, Controller Area Network (CAN) communication bandwidth has been pushed to its limit. Existing solutions - such as using multiple CAN buses, sending multiple CAN frames or switching to another protocol - require a significant system-design effort and replacement of hardware and software. This has paved the way for CAN Flexible Data-rate (CAN FD), which extends the CAN standard and permits significantly higher data rates, enabling faster firmware upgrades. However, some changes must be made like: exchange the classical CAN microcontroller with a CAN FD microcontroller and also a CAN FD transceiver must be used.</div><div class="htmlview paragraph">This article will explore the benefits of CAN FD, how it compares with the classical CAN protocol, requirements for systems and how to introduce CAN FD into existing CAN networks.</div></div>

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.

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 response time distributions of the frames sent on a Controller Area Network (CAN) bus are of prime interest to dimension and validate automotive electronic architectures. However, the existing work on the timing behaviour of the CAN network does not take into account that all the data exchanges between the Electronic Control Units (ECUs) are driven by different and independent clocks which are subject to clock drifts. This paper proposes a model for clock drifts and describes their impact on the CAN frame response time distributions. By implementing the clock drifts in a CAN simulation tool, we show experimentally that the response time distributions converge, for drift values chosen randomly within the same range on all ECUs, whatever the initial phasings between the sending nodes. Furthermore, we show that, as a result of the clock drifts, the situations leading to the worst case response times are transient.

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.

In this paper we describe how Controller Area Network (CAN) frames can be relayed over a wireless Internet connection, enabling remote access to the CAN buses of vehicles for applications in automotive telematics. This opens up many new possibilities for diagnostics, monitoring, testing, analysis and verification, which we believe can significantly reduce the time required for the testing and verification phases of automotive development. A CAN-over-IP tunneling protocol is described and the design and implementation of a generic system for remote access to the CAN bus of vehicles is presented. Examples of applications of the technology are given and implications in terms of new possibilities and challenges in automotive engineering are discussed.

The goal of this paper is to automate the creation of naturalistic driving data sets of dash camera footage that is tagged with information captured from the vehicle's Controller Area Network (CAN) bus, using only a standard dash camera and CAN reader. The paper describes pairing and synchronizing dash camera videos with CAN bus data gathered from a vehicle with advanced driver assistance features. That data is then used to label the dash camera videos with telemetric information. Further, with the synchronized videos and CAN bus data, it is possible to identify video clips with meaningful events such as following a lead vehicle, cars passing in front of the vehicle, braking, turns, etc. This method of data-gathering and data set creation is significantly cheaper and more scalable than other driving data sets, while having competitive quality in terms of telemetric attributes. This could significantly increase the quantity, diversity, and in turn, quality of driving data sets in the future.

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.

To enhance the attack resistance of the Controller Area Network (CAN) system and optimize the communication software design, a comprehensive model that combines a variable attacker with the CAN bus (VACB) is proposed to evaluate the bus communication risk. The VACB model consists of a variable attacker and the CAN bus model. A variable attacker is a visualized generation of the attack traffic based on a recurrent neural network (RNN), which is used to evaluate the anti-attack performance of the CAN bus; the CAN bus model combines the data link layer and the application layer to analyze the anomalies in CAN bus data transmission after the attack message. The simulation results indicate that the transmission accuracy and successful response rate decreased by 1.8% and 4.3% under the constructed variable attacker. The CAN bus’s authenticity was promoted after the developers adopted this model to analyze and optimize the software design. The transmission accuracy and the successful response rate were improved by 2.5% and 5.1%, respectively. Moreover, the model can quantify the risk of potential attacks on the CAN bus, prompting developers to avoid it in early development to reduce the loss caused by system crashes. The comprehensive model can provide theoretical guidance for the timing design of embedded software.

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 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 auxiliary functions of modern vehicles have increased the number of attack surfaces. As the most important information exchange channel for vehicles, Controller Area Network (CAN) attracts more and more attention. Due to no encryption and authentication, CAN bus is vulnerable to attack. Thus, the attacker can manipulate the message with serious consequences. Several methods have been proposed to improve the security of vehicles. However, most of them have their own deficiencies. In this paper, we propose an LOF-based attack detection scheme which utilizes the voltage physical characteristics of the CAN frame to judge whether a message is transmitted by a legitimate Electronic Control Unit (ECU). As validated by extensive experiments on two vehicles, our scheme can identify the attacker with an average detection rate of 98.9\%, and the false detection rate is less than 0.5\%. In addition, our scheme can accurately identify attacks from external devices.

Rec-CNN: In-vehicle networks intrusion detection using convolutional neural networks trained on recurrence plots