Preliminary results for introducing dependent random variables in stochastic feasibility analysis on CAN

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%.

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.

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 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.

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.

Controller Area Network (CAN) is a network that allows communication between ECUs in a vehicle by introducing a cheaper and light-weight communication medium. CAN is an asynchronous protocol yet provides efficient synchronous mechanism. Therefore, it is crucial to make sure the message transmission in CAN bus is accurate and does not miss its deadline. Local Interconnect Network (LIN) bus, is widely adopted as an alternative and additive bus protocol that has been used to complement CAN. In this project, the objective is to investigate the performance of the CAN signal generated from CAN simulator - CANOE, with and without the integration LIN. Moreover, this paper aims to observe the difference between these two different methods of signal transmission. As to create an experiment environment with the CAN and LIN simulation, the KWP Diagnostic Tester is created. Based on the established model, 4 cases have been introduced to create 4 different test environments, to showcase the different type of data transmission. It is shown that the CAN has an improved average of 2.7% on the transmission speed compared to CAN+LIN.

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%.

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.

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.

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.

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.

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.

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.

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.

As modern vehicles continue to evolve, advanced technologies are integrated to enhance the driving experience. A key enabler of this advancement is the Controller Area Network (CAN) bus, which facilitates seamless communication between vehicle components. Despite its widespread adoption, the CAN bus was not designed with security as a priority, making it vulnerable to various attacks. In this paper, we propose CANGuard, an Intrusion Detection System (IDS) designed to detect attacks on the CAN network and identify the originating node in real time. Using a simulated CAN-enabled system with four nodes representing diverse vehicle components, we generated a dataset featuring Denial-of-Service (DoS) attacks by exploiting the arbitration feature of the CAN bus, which prioritizes high-criticality messages (e.g., engine control) over lower-criticality ones (e.g., infotainment). We trained and evaluated several machine learning models for their ability to detect attacks and pinpoint the responsible node. Results indicate that Gradient Boosting outperformed other models, achieving high accuracy in both attack detection and node identification. While the Multi-Layer Perceptron (MLP) model demonstrated strong attack detection performance, it struggled with node identification, achieving less than 50% accuracy. These findings underscore the potential of tree-based models for real-time IDS applications in CAN-enabled vehicles.