Response Time Analysis for Nonperiodic CAN Message Based on GI/G/1 Queue Model

The Controller Area Networks (CAN) are widely used in industrial Networked Control Systems (NCSs), such as construction machineries, hybrid vehicles, robotics, and other applications. The message response time (MRT) or communication delay is the main issue to degrade the performance of CAN-based NCSs since its exact value is time-varying and unpredictable. The online acquisition of exact MRT can be quite helpful for the delay compensation of NCSs. However, since the clocks on different nodes are asynchronous, the MRT acquisition in CAN is a challenging work. The current delay acquisition methods for asynchronous systems are not suitable for the delay compensation in CAN-based NCSs because they either increase the bus load of CAN or cannot acquire the exact MRT in real time. In this paper, we propose a novel online real-time MRT estimation method for periodic CAN messages based on the analysis of message traces on CAN bus. The proposed method can estimate the exact MRT of the received message instance in real time without increasing the bus load and can be conveniently embedded into the CAN nodes without requiring additional equipment. In order to validate the proposed method, practical experiments are carried out and the experimental results show that the proposed method can effectively estimate the exact MRT of periodic CAN messages.

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%.

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) is widely employed in automotive control system networks. In the last few years, the amount of data has been increasing rapidly in these networks. With the purpose of improving CAN bandwidth efficiency, scheduling and analysis are considered to be particularly important. As an effective method, it has been known that assigning offsets to the CAN messages can reduce their worst case response time (WCRT). Meanwhile, the fact is that many commercial CAN controllers have been equipped with a priority or first-in-first-out (FIFO) queue to transmit messages to the CAN bus. However, previous researches for WCRT analysis of CAN messages either assumed a priority queue or did not consider the offset. For this reason, in this paper we propose a WCRT analysis method for CAN messages with assigned offset in the FIFO queue. We first present a critical instant theorem, then we propose two algorithms for WCRT calculation based on the given theorem. Experimental results on generated message sets and a real message set have validated the effectiveness of the proposed algorithms.

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.

A probabilistic approach to determine response-time distribution for messages in Controller Area Network (CAN) is presented here. CAN is a field bus level communication network for exchanging short real-time messages. CAN is mostly used to carry periodic messages for control and automation systems. Traditional response-time analysis of CAN messages only gives worst-case response-times. This introduces pessimism in analysis, resulting in over designing and under utilization of resources. Response-time distribution is a probabilistic function. Response-time distribution provides a complete characterization of CAN message response-time. It can provide probability of meeting/missing any specified response-time limit. This probability is useful for system reliability and performance modeling. In this paper response-time model of CAN messages is discussed. Technique to derive probabilistic parameters of response-time model, for periodic CAN messages with deterministic transmission times is given. The method has been applied to a standard data set, results are compared with that of worst-case analysis and reasons for deviations are discussed.

The rapid integration of connectivity and intelligence in modern vehicles has significantly increased the attack surface of in-vehicle networks, particularly the Controller Area Network (CAN) bus. Traditional intrusion detection systems (IDS) often struggle to achieve high detection accuracy while maintaining low computational overhead suitable for real-time automotive environments. To address these challenges, this paper proposes a modified Enhanced Cuckoo Filter–based Intrusion Detection System, referred to as a Quantum-Inspired Ensemble IDS, for secure CAN bus communication. In the proposed approach, a self-learning enhanced cuckoo filter (ECF) is employed as a fast pre-filtering mechanism to efficiently separate known normal and malicious CAN traffic patterns, thereby reducing unnecessary deep model computations. Subsequently, a quantum-inspired feature enhancement module is introduced to enrich CAN message representations using simulated quantum rotation and superposition concepts. The enhanced features are then processed by an ensemble of deep learning models, including a Temporal Attention Transformer, CNN-LSTM network, Quantum-Inspired Neural Network, and a hybrid deep model, whose outputs are combined using weighted voting for final intrusion classification. The proposed system is evaluated using the Car Hacking Dataset (CHD) and the CAN train-and-test dataset under multiple attack scenarios such as DoS, fuzzy, gear, and RPM attacks. Experimental results demonstrate that the proposed method achieves over 99.9% detection accuracy, lower detection latency (<1 ms per sample), and reduced CPU utilization compared to existing ECF-IDS and state-of-the-art CAN intrusion detection methods. Realistic performance evaluation confirms that the proposed system is highly effective, computationally efficient, and suitable for real-time deployment in automotive environments.

The vehicle industry is quickly becoming more connected and growing. This growth is due to advancements in cyber physical systems (CPSs) that enhance the safety and automation in vehicle. The modern automobile consists of more than 70 electronic control units (ECUs) that communicate and interact with each other over automotive bus systems. Passenger comforts, infotainment features, and connectivity continue to progress through the growth and integration of Internet-of-Things (IoT) technologies. Common networks include the Controller Area Network (CAN), Local Interconnect Network (LIN), and FlexRay. However, the benefits of increased connectivity and features comes with the penalty of increased vulnerabilities. Security is lacking in preventing attacks on safety-critical control systems. I will explore the state of the art methods and approaches researchers have taken to identify threats and how to address them with intrusion detection. I discuss the development of a hybrid based intrusion detection approach that combines anomaly and signature based detection methods. Machine learning is a hot topic in security as it is a method of learning and classifying system behavior and can detect intrusions that alter normal behavior. In this paper, we discuss utilizing machine learning algorithms to assist in classifying CAN messages. I present work that focuses on the reverse engineering and classification of CAN messages. The problem is that even though CAN is standardized, the implementation may vary for different manufacturers and vehicle models. These implementations are kept secret, therefore CAN messages for every vehicle needs to be analyzed and reverse engineered in order to get information. Due to the lack of publicly available CAN specifications, attackers and researchers need to reverse engineer messages to pinpoint which messages will have the desired impact. The reverse engineering process is needed by researchers and hackers for all manufacturers and their respective vehicles to understand what the vehicle is doing and what each CAN message means. The knowledge of the specifications of CAN messages can improve the effectiveness of security mechanisms applied to CAN.

Controller Area Network (CAN) is used extensively in automotive applications, with in excess of 400 million CAN enabled microcontrollers manufactured each year. In 1994 schedulability analysis was developed for CAN, showing how worst-case response times of CAN messages could be calculated and hence guarantees provided that message response times would not exceed their deadlines. This seminal research has been cited in over 200 subsequent papers and transferred to industry in the form of commercial CAN schedulability analysis tools. These tools have been used by a large number of major automotive manufacturers in the design of in-vehicle networks for a wide range of cars, millions of which have been manufactured during the last decade.This paper shows that the original schedulability analysis given for CAN messages is flawed. It may provide guarantees for messages that will in fact miss their deadlines in the worst-case. This paper provides revised analysis resolving the problems with the original approach. Further, it highlights that the priority assignment policy, previously claimed to be optimal for CAN, is not in fact optimal and cites a method of obtaining an optimal priority ordering that is applicable to CAN. The paper discusses the possible impact on commercial CAN systems designed and developed using flawed schedulability analysis and makes recommendations for the revision of CAN schedulability analysis tools.

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

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.

Controller area network (CAN) data-reduction techniques have been developed to reduce CAN bus loads, which have increased due to the large number of electronic control units in modern automobiles. Automotive communication uses various in-vehicle network. These techniques make it possible that same information to be sent using less bandwidth. However, the existing CAN DR algorithms only consider the CAN network. Automotive communication uses various in-vehicle network (IVN) protocols such as CAN, FlexRay, and Ethernet. The use of DR techniques may cause compatibility issue in heterogenous IVNs. This paper proposes a gateway system for data-reduced communication between Ethernet and CAN systems with data-reduction. The proposed gateway system receives a compressed CAN message, decompresses it, and transmits it to the Ethernet network. Additionally, the gateway receives a normal Ethernet message, compresses it, and transmits it to the CAN protocol. An evaluation of the performance of the developed gateway system is also presented.

The Controller Area Network (CAN) is considered as the de-facto standard for the in-vehicle communications due to its real-time performance and high reliability. Unfortunately, the lack of security protection on the CAN bus gives attackers the opportunity to remotely compromise a vehicle. In this paper, we propose a Lightweight Encryption and Authentication Protocol (LEAP) with low cost and high efficiency to address the security issue of the CAN bus. LEAP exploits the security-enhanced stream cipher primitive to provide encryption and authentication for the CAN messages. Compared with the state-of-the-art Message Authentication Code (MAC) based approaches, LEAP requires less memory, is 8X faster, and thwarts the most recently proposed attacks.

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.

Based on research of the communication process between vehicle BMS (Battery Management System) and charging pile during charging, and the detailed research of CAN (Controller Area Network) bus technical specifications, protocol standards and frame structure, fault detection method is determined. Based on this fault detection method, fault detection system of charging pile is designed. Wifi is utilized to send CAN messages of multiple charging piles to embedded devices to realize the joint monitoring scheme of multiple charging piles. By analyzing the CAN message content during charging, proposed system can analyze the electrical attributes in the charging process, realizes the real-time monitoring of charging pile. The equipment structure diagram, the overall logic block diagram and the software work flow diagram of real-time monitoring system are designed.