Controller Area Network Protocol Research Articles

CANASTA: Controller Area Network Authentication Schedulability Timing Analysis

The Controller Area Network (CAN) dominates in-vehicle networking systems in modern vehicles. CAN was designed with low-latency and reliability as key features. Authenticity of a CAN frame was not considered in the design, thus, most in-vehicle network nodes inherently trust received messages as coming from a legitimate source. As a result, it is trivial to program (or hack) a network node to spoof traffic. Authentication is challenging for CAN and related protocols, such as SAE J1939, due to limited frame sizes and high bus utilization. Adding a message authentication code (MAC) as a separate message can unduly stress the real-time delivery of safety-critical messages. Although this stressor is well-known, the impact of authentication protocols on real-time message delivery in CAN has not yet been thoroughly examined. In this paper, we provide the first comprehensive analysis of realtime schedulability analysis applied to authentication schemes for CAN, CAN Flexible Data-rate (CAN FD), and CAN extra long payload (CAN XL). We formulate the response time analysis for addition of MACs and periodic transmission of MACs, and we examine their impact on two case studies and through evaluation with randomized schedulability experiments over a wide range of message sets.

A Digital Watermark Method for In-Vehicle Network Security Enhancement

In recent years, automobiles have become increasingly networked with the development of information and communication technology (ICT). With the enhancement of the connectivity between the electronic control units of the vehicle and the external network, cybersecurity and driving safety issues have also arisen. However, as a traditional in-vehicle network standard, the controller area network (CAN) protocol lacks a network security mechanism design, making it vulnerable to hacker attacks. The current research on the cybersecurity enhancement method for in-vehicle CAN is a multi-objective optimization problem with response time, bandwidth consumption and computational complexity reduction. This study presents an in-vehicle message authentication method based on digital watermark and Huffman coding that are compatible with current CAN bus protocols. Our method not only minimize the delay of message authentication, but also save the storage resources to the greatest extent. Moreover, to tackle the issue of low computing resources, multiple messages can be authenticated at one time. In order to evaluate the performance, we implement our method on CAN communication platform and validate its high accuracy, low latency, and compatibility.

Design and implementation of a wireless CAN module for marine engines using ZigBee protocol

AbstractThis paper describes the design and implementation of a wireless control area network (CAN bus) protocol for communication between the smart NOx (nitrogen oxide) sensor on diesel engines and the engine control unit (ECU). In this research, the approach taken is based on a case study of Wärtsilä's smart NOx sensor on a W4L20 diesel engine with the objective of replacing the wired CAN protocol with a wireless CAN communication node. In the current setup, the smart NOx sensor is connected to the engine control unit (ECU) with a wired CAN bus connection. The XBee module, which uses the ZigBee (IEEE 802.15.4) technology was used in the design and implementation of the wireless CAN prototype. With the emergence of 5G networks and the era of IoT, the topic of wireless industrial automation becomes essential in the modern industry. In addition to the great advantages and opportunities that the use of wireless nodes has in automation systems, there are many real challenges. The practical design challenges have been addressed in this paper.

Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study.

Electronic Control Units (ECUs) have been increasingly used in modern vehicles to control the operations of the vehicle, improve driving comfort, and safety. For the operation of the vehicle, these ECUs communicate using a Controller Area Network (CAN) protocol that has many security vulnerabilities. According to the report of Upstream 2022, more than 900 automotive cybersecurity incidents were reported in 2021 only. In addition to developing a more secure CAN protocol, intrusion detection can provide a path to mitigate cyberattacks on the vehicle. This paper proposes a machine learning-based intrusion detection system (IDS) using a Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) and investigates the effectiveness of the IDS using multiple real-world datasets. The novelty of our developed IDS is that it has been trained and tested on multiple vehicular datasets (Kia Soul and a Chevrolet Spark) to detect and classify intrusion. Our IDS has achieved accuracy up to 99.9% with a high true positive and a low false negative rate. Finally, the comparison of our performance evaluation outcomes demonstrates that the proposed IDS outperforms the existing works in terms of its liability and efficiency to detect cyber-attacks with a minimal error rate.

An Automatic Irrigation Based on Control Area Network (CAN) Protocol

An Automatic Irrigation Based on Control Area Network (CAN) Protocol

MAC-Based Compression Ratio Improvement for CAN Security

Information security in a controller area network (CAN) is becoming more important as the connections between a vehicle’s internal and external networks increase. Encryption and authentication techniques can be applied to CAN data frames to enhance security. To authenticate a data frame, a message authentication code (MAC) needs to be transmitted with the CAN data frame. Therefore, space for transmitting the MAC is required within the CAN frame. Recently, the Triple ID algorithm has been proposed to create additional space in the data field of the CAN frame. The Triple ID algorithm ensures every CAN frame is authenticated by at least 4 bytes of MAC without changing the original CAN protocol. However, since the Triple ID algorithm uses six header bits, there is a problem associated with low data compression efficiency. In this paper, we propose an algorithm that can remove up to 15 bits from frames compressed with the Triple ID algorithm. Through simulation using CAN signals of a Kia Sorento vehicle and an LS Mtron tractor, we show that the generation of frames containing compressed messages of 4 bytes or more is reduced by up to 99.57% compared to the Triple ID method.

DT-DS: CAN Intrusion Detection with Decision Tree Ensembles

The controller area network (CAN) protocol, used in many modern vehicles for real-time inter-device communications, is known to have cybersecurity vulnerabilities, putting passengers at risk for data exfiltration and control system sabotage. To address this issue, researchers have proposed to utilize security measures based on cryptography and message authentication; unfortunately, such approaches are often too computationally expensive to be deployed in real time on CAN devices. Additionally, they have developed machine learning (ML) techniques to detect anomalies in CAN traffic and thereby prevent attacks. The main disadvantage of existing ML-based techniques is that they either depend on additional computational hardware or they heuristically assume that all communication anomalies are malicious. In this article, we show that tree-based learning ensembles outperform anomaly-based techniques like AutoRegressive Integrated Moving Average (ARIMA) and Z-Score when used to detect attacks that result in increased bus utilization. We evaluated the detection capacity of three tree-based ensembles, Adaboost, gradient boosting, and random forests, and collectively refer to these as DT-DS. We conclude that the decision tree ensemble with Adaboost performs best with an area under curve (AUC) score of 0.999, closely followed by gradient boosting and random forests with 0.997 and 0.991 AUC scores, respectively, when trained using message profiles. We observe that with an increase in the observation window, the DT-DS models present an average AUC score of 0.999, and offer a nearly perfect detection of attacks, at the cost of increased latency in detection of attacked messages. We evaluate the performance of the IDS for Aeronautical Radio, Incorporated– (ARINC) encoded CAN communication traffic in avionic systems, generated using an aerospace testbench, ARINC-825TBv2. The IDS has been evaluated against the active attacks of a state-of-the-art predictive attacker model. Additionally, we observed that the performance of IDS approaches such as ARIMA and Z-Score degrade considerably with a decrease in the size of the observation time window. In contrast, the performance of DT-DS models is consistent, with only an average drop of 0.005 in the AUC score.

Flooding attack mitigator for in-vehicle CAN using fault confinement in CAN protocol

For driver convenience and safety, a number of electronic control units (ECUs) have been installed on modern vehicles. To support communications among ECUs, the controller area network (CAN) is commonly used as in-vehicle network for several decades. However, the CAN protocol lacks security mechanisms, which means that it can be damaged by a number of cyber attacks. In particular, message flooding is one type of DoS attack known to be the easiest to perform because it continuously broadcasts a large number of CAN messages to the in-vehicle CAN without any CAN traffic analysis. To handle message flooding on the in-vehicle CAN, several countermeasures including intrusion detection and prevention have been studied, but unfortunately, these solutions could produce false positive detection rates or cause the communication failures of benign ECUs. In this paper, we introduce a message flooding attack mitigation method for the first time that does not accidentally cause the communication failures of benign ECUs. The proposed method can mitigate flooding attack attempts by using the fault confinement rule that is defined in the CAN protocol. Since the proposed method does not violate the rules of the CAN standard during mitigating, no system modifications are required. Experimental results show that the proposed mitigator guarantees a transmission rate up to 79.22% of normal messages that were not sent due to a flooding attack.

Unsupervised Deep Learning Approach for In-Vehicle Intrusion Detection System

The controller area network (CAN) is a standard communication protocol used for sending messages between electronic control unit of a modern automotive system. CAN protocol does not have any in-built security mechanisms and, hence, various attacks can affect the vehicle and cause life threats to the passengers. This article presents an unsupervised deep learning architecture for detecting intrusions on a CAN bus. The CAN intrusion detection system (IDS) architecture has an autoencoder that helps to learn the optimal features from CAN packets to differentiate between the normal and attacks. The optimal features are passed as input to the Gaussian mixture model, which helps us to cluster the CAN network packet data samples into normal and attacks. A detailed analysis of the proposed architecture is done on the CAN IDS dataset. To develop a robust CAN IDS system and achieve generalization, the proposed method is evaluated on the other two computer network intrusion datasets and a wireless sensor network dataset. In all the experiments, the proposed method has performed better than the existing unsupervised method and mainly showed a performance gain of 6.4% on the CAN IDS dataset. This indicates that the proposed method is robust and generalizable across detecting various attacks in a CAN bus and most importantly, the method can be used in real time to effectively monitor the CAN network traffic to proactively alert possible attacks.

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.

NovelADS: A Novel Anomaly Detection System for Intra-Vehicular Networks

Modern vehicular electronics is a complex system of multiple Electronic Control Units (ECUs) communicating to provide efficient vehicle functioning. These ECUs communicate using the well-known Controller Area Network (CAN) protocol. The increasing amount of research in the Intelligent Transportation System (ITS) domain has demonstrated that this protocol is vulnerable to various types of security attacks, compromising the safety of passengers and pedestrians on the roads. Hence, there is a need to develop novel anomaly detection systems to address this problem. This work presents a novel deep learning-based Intrusion Detection System incorporating thresholding and error reconstruction approaches. We train and explore multiple neural network architectures and compare their performance. The proposed anomaly detection system is tested on four kinds of attacks - Denial of Service (DoS), Fuzzy, RPM Spoofing and Gear Spoofing using evaluation metrics such as Precision, Recall and F1-Score. We also present reconstruction-error distribution plots to give a qualitative intuition about the proposed system’s ability to distinguish between genuine and anomalous sequences.

The Monitoring of Pixel System (MOPS) chip for the Detector Control System of the ATLAS ITk Pixel Detector

The ATLAS experiment will get a new inner tracker (ITk) during the phase II upgrade. The innermost part is called the Pixel Detector. A new Detector Control System (DCS) is being developed to provide control and monitoring of the ITk pixel detector. The Monitoring of Pixel System (MOPS) chip is an Application Specific Integrated Circuit (ASIC) foreseen in the DCS to independently monitor the voltage and the temperature across the modules which constitute the front-end electronics responsible for tracking and data readout. The chip is developed by the Bergische Universität Wuppertal in collaboration with the Fachhochschule Dortmund. The modules which need to be monitored are powered serially in a chain. The MOPS chip has a 12-bit ADC which can read up to 34 channels. Controller Area Network (CAN) and CANopen protocols are used for communication. The final chip is required to be radiation hard up to an ionizing dose of 500 Mrad. In this paper, the functionality of the chip will be discussed, some results from the first version of the chip will be presented as well as a brief overview of the second version.

Analysis of Transportation Network Scheduling of Maritime Silk Road and International Digital Trade Potential Based on Improved Genetic Algorithm under Digital Trade Barriers

The purpose is to improve the ship scheduling capability of the Maritime Silk Road transportation network and advance the international digital trade capabilities along China’s Maritime Silk Road. The optimization process of ship network scheduling based on a simulated annealing- (SA-) improved genetic algorithm (GA) is studied. Then, according to the time-varying stochastic frontier gravity model, the potential of international digital trade between China and the countries on the western route of the Maritime Silk Road is analyzed. The model is simulated and verified. The results reveal that the improved GA has obvious advantages over other algorithms in the message delivery rate of the high-speed and low-speed TT Controller Area Network (TTCAN) protocol for ship control. Regardless of the external environment and the number of message nodes, the message delivery rate is above 95%, showing a high delivery rate and the best network scheduling effect. The total volume and proportion of trade between China and the countries along the western route of the Maritime Silk Road can represent a good growth trend over time, and the proportion of digital trade exports has increased from 9.75% in 2009 to 45.12% in 2020. China has great potential for the development of the countries along the Silk Road as a whole. The main contribution of the research is to construct the ship network scheduling optimization process of the GA improved by the SA and use the time-varying stochastic frontier gravity model to study the digital trade potential between China and the countries along the Maritime Silk Road. The data information management system for network dispatching of marine ships has been improved, and the optimal dispatching efficiency has been enhanced.

Design and Simulation of Controller Area Network Module Using Verilog HDL

Abstract: Controller Area Network or CAN protocol is a method of communication between various electronic devices. It has significant use in the industry of automobiles. It defines a standard for efficient and reliable communication between sensor, actuator, controller, and other nodes in real-time applications. This provides a mechanism which is incorporated in the hardware and the software by which different electronic modules can communicate with each other using a common cable. CAN protocol is a message-based protocol, not an address-based protocol. All nodes in the system receive every message transmitted on the bus (and will acknowledge if the message was properly received). It is up to each node in the system to decide whether the message received should be immediately discarded or kept to be processed. The main aim is to design, development, and implementation of a CAN module using Verilog HDL. The intent is to show that the designed CAN module can replace a standalone CAN controller which occupies extra space in the system. Also implementing the CAN with the help of custom RAM is aimed at removing the need for FPGA specific RAM. The proposed design of CAN module is an integration of lower-level modules. The desired Verilog implementation of CAN module can help in simulating different systems at the design level instead of using physical ICs. The design includes a simple scheme that aims in reduction of circuit complexity and chances of hardware failure without requiring any extra logic circuitry.

Detecting in-vehicle intrusion via semi-supervised learning-based convolutional adversarial autoencoders

With the development of autonomous vehicle technology, the controller area network (CAN) bus has become the de facto standard for an in-vehicle communication system because of its simplicity and efficiency. However, without any encryption and authentication mechanisms, the in-vehicle network using the CAN protocol is susceptible to a wide range of attacks. Many studies, which are mostly based on machine learning, have proposed installing an intrusion detection system (IDS) for anomaly detection in the CAN bus system. Although machine learning methods have many advantages for IDS, previous models usually require a large amount of labeled data, which results in high time and labor costs. To handle this problem, we propose a novel semi-supervised learning-based convolutional adversarial autoencoder model in this paper. The proposed model combines two popular deep learning models: autoencoder and generative adversarial networks. First, the model is trained with unlabeled data to learn the manifolds of normal and attack patterns. Then, only a small number of labeled samples are used in supervised training. The proposed model can detect various kinds of message injection attacks, such as DoS, fuzzy, and spoofing, as well as unknown attacks. The experimental results show that the proposed model achieves the highest F1 score of 0.9984 and a low error rate of 0.1% with limited labeled data compared to other supervised methods. In addition, we show that the model can meet the real-time requirement by analyzing the model complexity in terms of the number of trainable parameters and inference time. This study successfully reduced the number of model parameters by five times and the inference time by eight times, compared to a state-of-the-art model.

ClockIDS: A Real-Time Vehicle Intrusion Detection System Based on Clock Skew

Although intelligent connected vehicles (ICVs) can better assist drivers and improve their driving experience, they have huge network security problems and are frequently attacked. This is because the vehicle network is connected to the Internet, which expands the attack surface of ICV, and attackers have more ways to launch attacks. In recent years, many security experts fight against attackers and propose various types of vehicle intrusion detection systems (IDSs) to protect the controller area network (CAN). However, with the continuous enhancement of attack means, especially the appearance of the masquerade attack, most IDSs are no longer applicable. In this article, we design a new fingerprint-based vehicle IDS to protect the CAN, called ClockIDS. It establishes a unique fingerprint for each electronic control unit (ECU) based on clock skew. On this basis, ClockIDS realizes the functions of intrusion detection and attack source identification by utilizing the empirical rule and dynamic time warping. It neither occupies the bandwidth of CAN bus nor needs to modify the CAN protocol. Our experiments on two real vehicles show that ClockIDS can establish a unique fingerprint for ECU without being affected by the size of message period, and can detect three types of attack with a detection accuracy of 98.63%. In addition, this system can identify the attack source, and the average recognition accuracy is 96.77%. Furthermore, ClockIDS has high real-time performance, and the average time cost of each detection is only 1.99 ms.

PUF-Based Post-Quantum CAN-FD Framework for Vehicular Security

The Controller Area Network (CAN) is a bus protocol widely used in Electronic control Units (ECUs) to communicate between various subsystems in vehicles. Insecure CAN networks can allow attackers to control information between vital vehicular subsystems. As vehicles can have lifespans of multiple decades, post-quantum cryptosystems are essential for protecting the vehicle communication systems from quantum attacks. However, standard CAN’s efficiency and payload sizes are too small for post-quantum cryptography. The Controller Area Network Flexible Data-Rate (CAN-FD) is an updated protocol for CAN that increases transmission speeds and maximum payload size. With CAN-FD, higher security standards, such as post-quantum, can be utilized without severely impacting performance. In this paper, we propose PUF-Based Post-Quantum Cryptographic CAN-FD Framework, or PUF-PQC-CANFD. Our framework provides post-quantum security to the CAN network while transmitting and storing less information than other existing pre-quantum and post-quantum CAN frameworks. Our proposal protects against most cryptographic-based attacks while transmitting (at up to 100 ECUs) 25–94% less messages than existing pre-quantum frameworks and 99% less messages than existing post-quantum frameworks. PUF-PQC-CANFD is optimized for smaller post-quantum key sizes, storage requirements, and transmitted information to minimize the impact on resource-restricted ECUs.

High Accuracy Software-Based Clock Synchronization Over CAN

In distributed real-time communication systems, common knowledge of the global time is crucial. It prevents message violations on the bus and allows independent components to collaborate within a real-time system on a timely basis. Systems with hard real-time requirements need to have high precision and accuracy of time. This is achieved by hardware-supported frame time-stamping mechanisms as found in dedicated protocols, such as time-triggered CAN (TTCAN), Flexray, and time-sensitive networking (TSN)-enabled Ethernet. However, many microcontroller units are not specifically designed to provide such a hardware-based solution at the communication interface. Therefore, a software-based implementation of the time synchronization algorithm is needed. Nevertheless, some commercial off-the-shelf (COTS) microcontroller units already provide an IEEE 1588-enabled Ethernet interface, including a high-precision timer module with rate correction. This module can be used for time synchronization purposes to align a set of distributed clocks via various communication interfaces. This article investigates the accuracy of software-based and hardware-supported time synchronization algorithm over the controller area network (CAN) protocol using a COTS microcontroller. As a result, we present identified jitter and delay sources as well as the achieved time accuracy. We show that using an advanced timer module combined with additional system knowledge allows submicrosecond precision and accuracies.

IoT-Based Response Time Analysis of Messages for Smart Autonomous Collision Avoidance System Using Controller Area Network

Many accidents and serious problems occur on the road due to the rapid increase in traffic congestion in all sections of the country. Autonomous vehicles provide a solution to successfully and cost-effectively avoid this problem while minimizing user disruption. Currently, more engaging electromechanical elements with an analog interface are used to develop affordable automobiles for efficient and cost-effective operation for a smart driving platform with a semiautonomous automobile, strengthening the vehicle involvement of the driver while increasing safety. As a result, it takes longer for various car elements to respond, which causes more problems during message transmission. This project aims to create a Controller Area Network (CAN) for analyzing message response times by incorporating a few application nodes on the IoT platform, such as an antilock braking system, flexible cruise control, and seat belt section, for some real-time control system applications. These application nodes are car analytical parts that are linked to IoT modules to prevent collisions. An autonomous device for collision avoidance and obstacle detection in a vehicle can impact road accidents if the CAN protocol is implemented.

MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach

Controller area network (CAN) bus which provides efficient, reliable and robust communication between electronic control units (ECUs) is the most frequently used protocol for in-vehicle networks. However, the lack of security mechanisms in the CAN protocol makes it vulnerable to inside and outside cyber-attacks. Therefore, an intrusion detection system (IDS), which is a widely used method to detect malicious activities, is preferred to improve the security of the CAN buses. In spite of the fact that various supervised and unsupervised machine learning algorithms are employed to increase the performance of IDSs, obtaining high classification performance is still a challenge for them. First, a lot of irrelevant and redundant features in the datasets result in long computational times with low detection performances. Second, different classification performances are acquired based on classifiers and a combination of the features. Third, many of the models suffer from unknown and different types of attacks. For these reasons, a new intrusion detection framework is proposed in this paper based on feature selection and classifier. Initially, we propose a meta-heuristic algorithm called modified genetic algorithm (MGA) m-feature selection for dimension reduction by selecting optimal feature subset based on k-fold cross validation. Then, we utilize five different linear and nonlinear classifiers: support vector classifier (SVC), logistic regression classifier (LRC), decision tree classifier (DTC), k-nearest neighbors classifier (KNC), and linear discriminant analysis classifier (LDAC) as candidate classifiers to develop an efficient IDS. Finally, we select the best classifier from the candidates and build an IDS. The experimental results reveal that the proposed MGA-DTC presents a better performance in terms of several metrics based on not only the HCRL-car hacking dataset but also UNSW-NB15, and CIC-IDS2017 datasets.