In-vehicle Communication Protocol Research Articles

Addressing Vulnerabilities in CAN-FD: An Exploration and Security Enhancement Approach

The rapid advancement of technology, alongside state-of-the-art techniques is at an all-time high. However, this unprecedented growth of technological prowess also brings forth potential threats, as oftentimes the security encompassing these technologies is imperfect. Particularly within the automobile industry, the recent strides in technology have brought about increased complexity. A notable flaw lies in the CAN-FD protocol, which lacks robust security measures, making it vulnerable to data theft, injection, replay, and flood data attacks. With the rising complexity of in-vehicular networks and the widespread adoption of CAN-FD, the imperative to safeguard the protocol has never been more crucial. This paper aims to provide a comprehensive review of the existing in-vehicle communication protocol, CAN-FD. It explores existing security approaches designed to fortify CAN-FD, demonstrating multiple multi-layer solutions that leverage modern techniques including Physical Unclonable Function (PUF), Elliptical Curve Cryptography (ECC), Ethereum Blockchain, and Smart contracts. The paper highlights existing multi-layer security measures that offer minimal overhead, optimal performance, and robust security. Moreover, it identifies areas where these security measures fall short and discusses ongoing research along with suggestions for implementing software and hardware-level modifications. These proposed changes aim to streamline complexity, reduce overhead while ensuring forward compatibility. In essence, the methods outlined in this study are poised to excel in real-world applications, offering robust protection for the evolving landscape of in-vehicular communication systems.

A Model-Based Method for Enabling Source Mapping and Intrusion Detection on Proprietary Can Bus

With the deep integration of the Internet of Things (IoT) technology and the increase of computational power and memory, vehicles can also serve as the infrastructures for Intelligent Transportation System (ITS), e.g., as fog nodes. However, when connecting vehicles to the internet, alongside with the benefits it brings, it also opens many new challenges such as security attacks. Controller Area Network (CAN) is one of the main in-vehicle communication protocols in modern cars. Its lack of sender verification mechanism makes CAN particularly vulnerable to cyber-attacks including masquerade attack. Fingerprinting Electronic Control Units (ECUs) based on hardware characteristics has been proved feasible and effective on defending CAN buses. However, most state-of-the-art works exploited the supervised learning algorithm to identify the transmitter based on the signal characteristics. This makes the decision process hard to understand, and it also limits the deployment on proprietary CAN bus without prior knowledge. To solve this, we design a novel clock-skew-based approach capable of pinpointing the sender and detecting intrusion on proprietary CAN bus. We take a single CAN frame as the object for measurement, and adjust the measuring process such that our approach can be independent of the transmission time of frames. Based on the statistical analysis of data from real vehicles, we propose a novel box-plot algorithm based on score mechanism to filter the raw data. Finally, the clock skews are estimated and accumulated to build a linear model for representing the transmitter ECU. The evaluation results on one CAN prototype and two production vehicles show that our approach is able to well identify and differentiate ECUs on the bus without prior knowledge. The data processed by the proposed box-plot algorithm can describe the hardware characteristics of ECUs precisely. We also show the ability of our approach to protecting the CAN bus against the masquerade attack.

CANAttack: Assessing Vulnerabilities within Controller Area Network.

Current vehicles include electronic features that provide ease and convenience to drivers. These electronic features or nodes rely on in-vehicle communication protocols to ensure functionality. One of the most-widely adopted in-vehicle protocols on the market today is the Controller Area Network, popularly referred to as the CAN bus. The CAN bus is utilized in various modern, sophisticated vehicles. However, as the sophistication levels of vehicles continue to increase, we now see a high rise in attacks against them. These attacks range from simple to more-complex variants, which could have detrimental effects when carried out successfully. Therefore, there is a need to carry out an assessment of the security vulnerabilities that could be exploited within the CAN bus. In this research, we conducted a security vulnerability analysis on the CAN bus protocol by proposing an attack scenario on a CAN bus simulation that exploits the arbitration feature extensively. This feature determines which message is sent via the bus in the event that two or more nodes attempt to send a message at the same time. It achieves this by prioritizing messages with lower identifiers. Our analysis revealed that an attacker can spoof a message ID to gain high priority, continuously injecting messages with the spoofed ID. As a result, this prevents the transmission of legitimate messages, impacting the vehicle's operations. We identified significant risks in the CAN protocol, including spoofing, injection, and Denial of Service. Furthermore, we examined the latency of the CAN-enabled system under attack, finding that the compromised node (the attacker's device) consistently achieved the lowest latency due to message arbitration. This demonstrates the potential for an attacker to take control of the bus, injecting messages without contention, thereby disrupting the normal operations of the vehicle, which could potentially compromise safety.

Security assessment of in-vehicle communication protocols

As the automotive industry has progressed in recent years, in-vehicle networks have also changed. In contrast to traditional vehicles, modern vehicles communicate through connecting interfaces, Electronic Control Units (ECUs), sensors, and gateways over different communication channels called buses. While LIN, CAN, FlexRay, MOST, and Automotive Ethernet protocols facilitate vehicle communication, they expose vehicles to various security risks. Electronics in vehicles must be advanced to improve passenger safety, comfort, and vehicle performance. However, security issues must also be addressed to ensure vehicle resilience. This paper presents an in-depth and comprehensive analysis of the security issues associated with in-vehicle network protocols. We examine the feasibility, severity, and potential consequences of these security issues, which can be used to better understand the risks and vulnerabilities within in-vehicle networks. Furthermore, the paper examines existing solutions that aim to mitigate the identified attacks, including intrusion detection systems (IDSs), firewalls, authentication, and schedulers, while evaluating their effectiveness and drawbacks. Hence, we enable automotive security engineers to comprehend the current state of security measures used to protect in-vehicle networks and identify areas where further research is needed to develop more effective and tailored security solutions. Finally, the paper provides recommendations for future research and the development of security solutions that researchers can use to guide their research efforts for in-vehicle network security.

A multidisciplinary detection system for cyber attacks on Powertrain Cyber Physical Systems

With the introduction of external connectivity to modern vehicles, both academic and industry security practitioners addressed the issues related to external communication interfaces exposing internal networks that are used to control the different vehicle’s systems. Despite these issues are considered having little-to-no impact on the functioning of the vehicle itself due to their demand of physical connection, with the advent of cellular connectivity proof-of-concept and demonstrations of remote takeover of the vehicle have been showcased, raising awareness about the risks related to the introduction of external connectivity on the vehicle system. All these remote attacks demonstrated by security researchers exploited several vulnerabilities to gain remote access to the Controller Area Network (CAN), one of the most deployed in-vehicle communication protocol. In these works, the authors demonstrate how to exploit the drive-by-wire features of modern vehicles to jeopardize people inside and outside the vehicle. To address this issue, several researchers designed algorithms to detect the injection of malicious CAN messages. However, these solutions are focused on the only CAN communication, without considering all the other subsystems that can be used by an attacker to hijack the vehicular system. Due to their high complexity, modern vehicle can be seen as Cyber–Physical Systems, in which the physical system (i.e. the mechanics of the vehicle) are controlled via software hosted on microcontrollers that communicate with each other via different communication protocols. Hence, to properly address the cybersecurity of a modern vehicle it is necessary to consider all the different aspects of the system, without focusing on a single one. In this work we investigate the capabilities of a multidisciplinary approach for anomaly detection based for securing the powertrain system of a generic vehicle. The experimental analysis presented in this paper is based on the model of a generic internal combustion engine and a speed controller, communicating via CAN. Four different detection methods from both control theory and computer security are tested against cyber attacks carried out from different contexts. Our threat model is composed by different attack scenarios representative of known cyber-attacks targeting modern vehicles. The experimental evaluation presented in this work highlights the strengths and weaknesses of each detection method against different attack scenarios carried out on the different components of our system. To the best of our knowledge, this is the first paper addressing the cyber security of the connected vehicle with a multidisciplinary approach.

Intrusion Detection in Intelligent Connected Vehicles Based on Weighted Self-Information

The Internet of Vehicles (IoV) empowers intelligent and tailored services for intelligent connected vehicles (ICVs). However, with the increasing number of onboard external communication interfaces, ICVs face the challenges of malicious network intrusions. The closure of traditional vehicles had led to in-vehicle communication protocols, including the most commonly applied controller area network (CAN), and a lack of security and privacy protection mechanisms. Therefore, to protect the connected vehicles and IoV systems from being attacked, an intrusion-detection method is proposed based on the features extracted from the arbitration identifier (ID) field of CAN messages. Specifically, a sliding window is used to continuously extract a frame of streaming CAN messages first. Afterward, the weighted self-information of the CAN message ID is defined, and both the weighted self-information and the normalized value of an ID are extracted as features. Based on the extracted features, a lightweight one-class classifier, the local outlier factor (LOF), is used to identify the outliers and detect malicious network intrusion attacks. Simulation experiments were conducted based on the public CAN dataset provided by the HCR Lab. The proposed method, using four different one-class classifiers, was analyzed, and it is also benchmarked with three information entropy-based intrusion-detection methods in the literature. The experimental results indicate that, compared to the benchmarks, the proposed method dramatically improves the detection accuracy for injection attacks, namely denial-of-service (DoS) and spoofing, especially when the number of injected messages is low.

AI-Based Intrusion Detection Systems for In-Vehicle Networks: A Survey

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyber attacks. Various Intrusion Detection Systems (IDSs) have been developed to detect these attacks. However, the high generalization capabilities of Artificial Intelligence (AI) make AI-based IDS an excellent countermeasure against automotive cyber attacks. This article surveys AI-based in-vehicle IDS from 2016 to 2022 (August) with a novel taxonomy. It reviews the detection techniques, attack types, features, and benchmark datasets. Furthermore, the article discusses the security of AI models, necessary steps to develop AI-based IDSs in the CAN bus, identifies the limitations of existing proposals, and gives recommendations for future research directions.

A holistic approach to power efficiency in a clock offset based Intrusion Detection Systems for Controller Area Networks

Controller Area Network (CAN) is an in-vehicle communication protocol, which provides an efficient and reliable communication link between Electronic Control Units (ECUs) in real time. Recent studies have shown that attackers can take remote control of the targeted vehicle by exploiting the vulnerabilities of the CAN protocol. Motivated by this fact, we propose an Intrusion Detection System (IDS), called Clock Offset-based IDS (COIDS), to monitor in-vehicle network activities to detect any intrusion. Precisely, COIDS measures and then exploits the clock offset of transmitter ECU’s clock for fingerprinting ECU. COIDS next leverages the derived fingerprints to construct a baseline of ECU’s normal clock behavior using an active learning technique. Based on the baseline of normal behavior, COIDS uses the Cumulative Sum method to detect any abnormal deviation in clock offset. Further, COIDS uses a sequential change-point detection technique to determine the exact time of intrusion. Generally, COIDS has to run on every ECU to monitor the network behavior. This can turn out to be a significant power overhead for a hardware-constrained ECU. Thus, we next develop a cooperative game model to optimize the active time duration of COIDS in an ECU. We performed exhaustive experiments on real world publicly available datasets primarily to assess the effectiveness of COIDS against a wide range of in-vehicle network attacks. Our results show that COIDS detects intrusions faster than the best performed IDS in the state-of-the-art. Further, the results show that our designed cooperative game model significantly reduces the power overhead of the ECU without compromising the performance.

Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach

The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach.

BTMonitor

With the rapid growth of connectivity and autonomy for today’s automobiles, their security vulnerabilities are becoming one of the most urgent concerns in the automotive industry. The lack of message authentication in Controller Area Network (CAN), which is the most popular in-vehicle communication protocol, makes it susceptible to cyber attack. It has been demonstrated that the remote attackers can take over the maneuver of vehicles after getting access to CAN, which poses serious safety threats to the public. To mitigate this issue, we propose a novel intrusion detection system (IDS), called BTMonitor (Bit-time-based CAN Bus Monitor). It utilizes the small but measurable discrepancy of bit time in CAN frames to fingerprint their sender Electronic Control Units (ECUs). To reduce the requirement for high sampling rate, we calculate the bit time of recessive bits and dominant bits, respectively, and extract their statistical features as fingerprint. The generated fingerprint is then used to detect intrusion and pinpoint the attacker. BTMonitor can detect new types of masquerade attack that the state-of-the-art clock-skew-based IDS is unable to identify. We implement a prototype system for BTMonitor using Xilinx Spartan 6 FPGA for data collection. We evaluate our method on both a CAN bus prototype and a real vehicle. The results show that BTMonitor can correctly identify the sender with an average probability of 99.76% on the real vehicle.

Vulnerability verification for illegal attack on secure CAN communication

Message authentication using MAC has been proposed in order to prevent illegal attacks against CAN which is the standard of in-vehicle communication protocol. It also prevents replay attacks by inserting counter number in MAC. However, it is difficult to synchronize with counter number. Thus, message authentication cannot be performed correctly. Therefore, the counter synchronization mechanism is required. Regarding in-vehicle system, security verification is important as well as safety evaluation. This study proposes a new replay attack focused on the counter synchronization. This study also implements CAN communication with MAC. Experiments using implemented CAN communication show that the proposed replay attack succeeds. Experiment results indicate that the counter synchronization is a key technique to achieve secure in-vehicle system.

Timing Analysis of Precedence Constraint Messages Scheduled With Slot Multiplexing Over Dynamic Segment of FlexRay

FlexRay offers high-speed reliable transmission in the vehicle domain. Thus, it is one of the most popular in-vehicle communication protocols for the applications that are safety-critical or applications with very low transmission time requirement. Due to the real-time constraint, the worst case end-to-end delay (EED) for such applications transmitting over a shared bus must be known. This paper addresses the problem of computation of EED for a given set of tasks with precedence constraint and corresponding messages, which are scheduled with slot multiplexing for event-triggered communication over the dynamic segment of FlexRay. First, computing precise delay has been shown to be an intractable problem. Subsequently, motivated by the intractability, an efficient estimation technique has been proposed to compute approximate EED. Unlike the common practice of considering the network in isolation, we present a comprehensive timing analysis, which considers the effects of task execution as well. Moreover, EED is allowed to be greater than interactivation time of task graph. The extensive simulation has been performed on the test cases generated with uniform as well as normal distribution. Note to Practitioners —In the past few decades, due to the large-scale use and sophistication of automotive electronic systems, various in-vehicle communication networks and protocols are also evolving. FlexRay is one such in-vehicle communication protocol, which provides the mechanism for the fault-tolerant high-speed communication over FlexRay bus. FlexRay is widely being employed for safety-critical applications in the automotive domain. For such applications, often the timing constraint is imposed with a bound on the worst case EED. Thus, timing properties of the designed network must be verified before applying it on the real domain such that the worst case EED for the propagation of an event at sensor end to actuator end is known. The proposed estimation technique can be used to compute approximate EED for an application scheduled with other applications over the dynamic segment of FlexRay. In addition, various guidelines for designing the network in order to reduce overestimation have also been presented.

A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks.

FlexRay is a next-generation in-vehicle communication protocol which works in real time with flexibility. The most common applications in FlexRay are high bandwidth. X-by-wire applications, such as brake by wire and throttle by wire. However, there is no mechanism which can prevent transient faults in the application layer of FlexRay. If a transient fault occurs during driving, this would be very dangerous; therefore, we propose a fast reliability scheduling algorithm (FRSA) to improve the communication reliability of FlexRay. The proposed method reduces the probability of transient faults in one clock cycle by using a retransmission mechanism to recover the transient errors, and further improves computational complexity using the lookup table method to ensure system reliability. In this paper, we analyze a related literature to establish the system reliability constraints needed to evaluate the necessary time and slot usage, and the proposed cost function is used to evaluate the performance and efficiency when the number of messages is increased. Experimental results show that the proposed FRSA reduces execution time by an average 70.76% and cost by an average 13.33% more than the other existing methods. This method can be useful to others, especially regarding research about periodic time-triggered communication systems.

Optimal scheduling of the flexray static segment based on two-dimensional bin-packing algorithm

FlexRay is a reliable and hard real-time in-vehicle communication protocol that is strongly promoted by car manufacturers as the de facto standard in the automotive domain. The protocol offers both a time-triggered and an eventtriggered architecture. This paper focuses on the optimal scheduling of the time-triggered component of FlexRay known as the static (ST) segment using a two-dimensional bin-packing technique. To maximize the bandwidth utilization in the ST segment, a fast heuristic as well as an efficient integer linear programming approach are proposed. Our methods directly schedule signals into slots including frame packing, according to signal-based data scheduling and the slot/ cycle multiplexing mechanisms presented by the latest version of the FlexRay protocol. The benefits of our proposed methods are demonstrated by extensive experiments on synthetic and an automotive X-by-wire system case study. An additional test case is examined to emphasize the superior performance of the proposed approach relative to that of existing optimal scheduling approaches.