In-vehicle Controller Area Network Research Articles

(Semi-)Automatically Parsing Private Protocols for In-Vehicle ECU Communications

In-vehicle electronic control unit (ECU) communications generally count on private protocols (defined by the manufacturers) under controller area network (CAN) specifications. Parsing the private protocols for a particular vehicle model would be of great significance in testing the vehicle’s resistance to various attacks, as well as in designing efficient intrusion detection and prevention systems (IDPS) for the vehicle. This paper proposes a suite of methods for parsing ECU private protocols on in-vehicle CAN network. These methods include an algorithm for parsing discrete variables (encoded in a discrete manner, e.g., gear state), an algorithm for parsing continuous variables (encoded in a continuous manner, e.g., vehicle speed), and a parsing method based on upper-layer protocols (e.g., OBD and UDS). Extensive verifications have been performed on five different brands of automobiles (including an electric vehicle) to demonstrate the universality and the correctness of these parsing algorithms. Some parsing tips and experiences are also presented. Our continuous-variables parsing algorithm could run in a semi-automatic manner and the parsing algorithm from upper-layer protocols could execute in a completely automatic manner. One might view the results obtained by our parsing algorithms as an important indicator of penetration testing on in-vehicle CAN network.

Design and Evaluation of a Reconfigurable ECU Architecture for Secure and Dependable Automotive CPS

The next generation of automobiles integrate a multitude of electronic control units (ECUs) to implement various automotive control and infotainment applications. However, recent works have demonstrated that these pervasively computerized modern automobiles are susceptible to security attacks that could compromise the physical safety of the driver and/or passengers. In this paper, we propose a novel ECU architecture for automotive cyber-physical systems (CPS) that simultaneously integrates both security and dependability primitives in the design with negligible performance, energy, and resources overhead. We implement our proposed ECU architecture on Xilinx Automotive (XA) Spartan-6 FPGA. We demonstrate the effectiveness of our proposed architecture using a steer-by-wire (SBW) application over controller area network (CAN) with flexible data rate (CAN FD) as a case study. We also optimize and implement a prior secure and dependable automotive work on NXP quad-core iMX6Q SABRE automotive board. We quantify the performance, energy, and error resilience of our proposed architecture for the SBW case study. Results reveal that our proposed architecture can attain a speedup of 47.9× while consuming 2.4× lesser energy than the optimized SABRE board implementation of security and dependability primitives. We further perform a comparative analysis of prior designs and the proposed ECU architecture for different in-vehicle networks, viz., CAN, CAN FD, and FlexRay. Results verify the feasibility as well as the superiority of the proposed ECU over other prior designs in terms of response time, energy efficiency, and error resilience.

Triple ID Flexible MAC for Can Security Improvement

The development of automotive-information & communication technology (ICT) convergence has resulted in various vehicle-based electrical/electronic (E/E) systems. An automotive E/E system consists of one or more electronic control units (ECUs), sensors and actuators. With the commercialization of connected/autonomous cars, vehicle-based wireless communication systems have appeared, and are expected to grow in popularity. This increases the number of attack surfaces that can potentially threaten in-vehicle controller area network (CAN). To combat the vulnerabilities of the CAN protocol, the CAN data field should be encrypted and transmitted with authentication codes. Recently, a method of transmitting authentication codes by modifying the CAN protocol was proposed. However, changing the original CAN protocol can cause serious problems in CAN systems. In this paper, to enhance CAN security, a data compression algorithm is used to reduce the data frame length so that there is space for a message authentication code (MAC) to be contained inside the data field. The proposed algorithm guarantees that all CAN frames are authenticated by a MAC of at least four bytes without any change of the original CAN protocol. Simulations using CAN data from Kia Sorento, Kia Soul, and LS Mtron vehicles show that the proposed algorithm works successfully with only a slight increase in the peak load.

Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus

Communication between the nodes in a vehicle is performed using many protocols. The most common of these is known as the Controller Area Network (CAN). The functionality of the CAN protocol is based on sending messages from one node to all others throughout a bus. Messages are sent without either source or destination addresses. Consequently, it is simple for an attacker to inject malicious messages. This may lead to some nodes malfunctioning or total system failure, which can affect the safety of the driver as well as the vehicle. Detecting intrusions is a challenging problem in the context of using CAN bus for in-vehicle communication. Most existing work focuses on the physical aspects without taking into consideration the data itself. Machine Learning (ML) tools, especially classification techniques, have been widely used to address similar problems. In this paper, we use and compare several ML techniques to deal with the problem of detecting intrusions in in-vehicle communication. An experimental study is performed using a real dataset extracted from a KIA Soul car. Compared to previous work, which focuses on detecting intrusions based on the physical aspect, this paper aims to concentrate on the application of data analysis and statistical learning techniques. Furthermore, the paper provides a comparative study of the most common ML techniques. The results show that the techniques under consideration in this paper outperform other techniques that have been used previously.

Zero-knowledge identity authentication for internet of vehicles: Improvement and application.

The popularity of Internet of Vehicles (IoV) has made people's driving environment more comfortable and convenient. However, with the integration of external networks and the vehicle networks, the vulnerabilities of the Controller Area Network (CAN) are exposed, allowing attackers to remotely invade vehicle networks through external devices. Based on the remote attack model for vulnerabilities of the in-vehicle CAN, we designed an efficient and safe identity authentication scheme based on Feige-Fiat-Shamir (FFS) zero-knowledge identification scheme with extremely high soundness. We used the method of zero-one reversal and two-to-one verification to solve the problem that FFS cannot effectively resist guessing attacks. Then, we carried out a theoretical analysis of the scheme’s security and evaluated it on the software and hardware platform. Finally, regarding time overhead, under the same parameters, compared with the existing scheme, the scheme can complete the authentication within 6.1ms without having to go through multiple rounds of interaction, which reduces the additional authentication delay and enables all private keys to participate in one round of authentication, thereby eliminating the possibility that a private key may not be involved in the original protocol. Regarding security and soundness, as long as private keys are not cracked, the scheme can resist guessing attacks, which is more secure than the existing scheme.

Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis.

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.

Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data

This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics.

CANsec: A Practical in-Vehicle Controller Area Network Security Evaluation Tool.

The Internet of Things (IoT) is an industry-recognized next intelligent life solution that increases the level of comfort, efficiency, and automation for citizens through numerous sensors, smart devices, and cloud stations connected physically. As an important application scenario of IoT, the Internet of Vehicles (IoV) plays an extremely critical role in the intelligent transportation field. In fact, the In-Vehicle Network of smart vehicles that are recognized as the core roles in intelligent transportation is currently the Controller Area Network (CAN). However, the In-Vehicle CAN bus protocol has several vulnerabilities without any encryption, authentication, or integrity checking, which severely threatens the safety of drivers and passengers. Once malicious attackers hack the vehicular gateway and obtain the access right of the CAN, they may control the vehicle based on the vulnerabilities of the CAN bus protocol. Given the severe security risk of CAN, we proposed the CANsec, a practical In-Vehicle CAN security evaluation tool that simulates malicious attacks according to major attack models to evaluate the security risk of the In-Vehicle CAN. We also show a usage case of the CANsec without knowing any information from the vehicle manufacturer.

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.

Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security

In this letter, we create two types of attacks to investigate in-vehicle network security: replay attack and amplitude-shift attack. We use these two attacks to create attack datasets from two attack-free in-vehicle controller area network bus datasets (dataset-I and dataset-II), which represent a collection of correlated time series data. We develop a long short-term memory (LSTM) neural-network-based model for detecting replay attack and amplitude-shift attack. For attacks on the dataset-I, the LSTM detection model achieves accuracy of 87.8% and 87.9%, and area under the precision-recall curve (AUPRC) of 0.63 and 0.88, for replay attack and amplitude-shift attack, respectively. For attacks on the dataset-II, the LSTM detection model achieves accuracy of 83.7% and 83.8%, and AUPRC of 0.53 and 0.75, for replay attack and amplitude-shift attack, respectively. Overall, the LSTM detection model shows improvement in accuracy, precision, recall, and AUPRC over the baseline detection models considered in this letter.

CAN-ADF: The controller area network attack detection framework

In recent years, there has been significant interest in developing autonomous vehicles such as self-driving cars. In-vehicle communications, due to simplicity and reliability, a Controller Area Network (CAN) bus is widely used as the de facto standard to provide serial communications between Electronic Control Units (ECUs). However, prior research reveals that several network-level attacks can be performed due to the lack of defense mechanisms in the CAN bus. In this work, we propose CAN Bus Message Attack Detection Framework (CAN-ADF) - a comprehensive anomaly generation, detection, and evaluation system for a CAN bus. In CAN-ADF, not only various anomalies and attack characteristics can be configured but also different detection methods, and visualization frameworks are provided to effectively detect those attacks and anomalies. For the detector, we employ both a rule-based approach crafted from dynamic network traffic characteristics and Recurrent Neural Networks (RNN). For evaluation, we use 7,875,791 in-vehicle CAN packets collected from real cars, KIA Soul and Hyundai Sonata. Our detection algorithm achieves accurate intrusion detection performance, with an average accuracy of 99.45% on CAN datasets, outperforming prior approach. Furthermore, we developed a visualization tool to validate the detection of anomalies by CAN-ADF and to find new patterns in the dataset.

Identify a Spoofing Attack on an In-Vehicle CAN Bus Based on the Deep Features of an ECU Fingerprint Signal

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.

Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles

The in-vehicle controller area network (CAN) bus is one of the essential components for autonomous vehicles, and its safety will be one of the greatest challenges in the field of intelligent vehicles in the future. In this paper, we propose a novel system that uses a deep neural network (DNN) to detect anomalous CAN bus messages. We treat anomaly detection as a cross-domain modelling problem, in which three CAN bus data packets as a group are directly imported into the DNN architecture for parallel training with shared weights. After that, three data packets are represented as three independent feature vectors, which corresponds to three different types of data sequences, namely anchor, positive and negative. The proposed DNN architecture is an embedded triplet loss network that optimizes the distance between the anchor example and the positive example, makes it smaller than the distance between the anchor example and the negative example, and realizes the similarity calculation of samples, which were originally used in face detection. Compared to traditional anomaly detection methods, the proposed method to learn the parameters with shared-weight could improve detection efficiency and detection accuracy. The whole detection system is composed of the front-end and the back-end, which correspond to deep network and triplet loss network, respectively, and are trainable in an end-to-end fashion. Experimental results demonstrate that the proposed technology can make real-time responses to anomalies and attacks to the CAN bus, and significantly improve the detection ratio. To the best of our knowledge, the proposed method is the first used for anomaly detection in the in-vehicle CAN bus.

Development of Automatic Verification Environment for In-Vehicle Controller Area Network

Due to the development of modern techniques, in the recent years, electronic vehicles and autopilot systems have beensignificant emerged in automobile and IT industrial. This leads the electronics automotive systems and auto-control systems consistedof a lot of high performance Electronic Control Units(ECUs) connected by controller area network (CAN). For realizing morecomplicated design in ECUs, this work integrates real-time OS and network management function. The results improve the CANbusnodes' designing level to as a gateway to interconnect CANbus nodes. As the number of CANbus nodes increase, the verification processis more and more complicated and takes much time. For speeding up the verification process, this work uses CANoe package toprogram the testing script for automotive verification environment. Then the engineer can connect the testing device by CAN to theenvironment for automatic verification. The engineer can define the network messages of the CANbus nodes and tune the design asthe validating progress. The testing results present as XML format and can be transferred to HTML pages for readability. Hence, thiswork realizes an automatic verification environment for CANbus in-vehicle networks.

NOTSA: Novel OBU With Three-Level Security Architecture for Internet of Vehicles

Internet of Vehicles has become a massive network, which enables multinetwork fusion, including in-vehicle network, wireless local area network, dedicated short range communication, device-to-device communication, and cellular network (e.g., 5G). Proverbially, in-vehicle network is considered as a closed network and thus absolutely secure. However, multiple networks access and fusion can introduce different threats. Current on board units (OBUs) fail to protect in-vehicle network. Therefore, illegal vehicle control can be easily performed for attackers. In this paper, in consideration of vulnerabilities of current OBUs and in-vehicle controller area network, we show main attack model for actual vehicles, and assess introduced threats using the approach based on ISO 13335 guidelines for the management of IT security. Then, we design the novel OBU with three-level security architecture (NOTSA) and propose multilevel security protocols. It cannot only improve the security of external networks, but also protect the in-vehicle network by collaborating with current in-vehicle security schemes. Thereafter, we analyze security of proposed scheme, and evaluate it on hardware platform. The reliability of NOTSA and vehicular ad-hoc networks are then discussed using reliability block diagrams. In addition, we also verify the correctness of proposed protocols. Finally, the simulation results and comparison show that our scheme is feasible and more efficient than existing schemes in term of time overhead, security and reliability.

Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks

As the functions of vehicles are more computerized for the safety and convenience of drivers, attack surfaces of vehicle are accordingly increasing. Many attack results have shown that an attacker could intentionally control vehicles. Most of them exploit the vulnerability that controller area network (CAN) protocol, a de-facto standard for the in-vehicle networks, does not support message origin authentication. Although a number of methods to resolve this security vulnerability have been suggested, they have their each limitation to be applied into the current system. They have required either the modification of the CAN standard or dramatical communication load increase, which is infeasible in practice. In this paper, we propose a novel identification method, which works in the physical layer of the in-vehicle CAN network. Our method identifies electronic control units (ECUs) using inimitable characteristics of electrical CAN signals enabling detection of a malicious ECU. Unlike previous attempts to address the security problem in the in-vehicle CAN network, our method works by simply adding a monitoring unit to the existing network, making it deployable in current systems and compliant with required CAN standards. Our experimental results show that our method is able to correctly identify ECUs. In case of misclassfication rate for ECU idnetification, our method yields 0.36% in average which is approximate four times lower than the method proposed by P.-S. Murvay et al. This paper is also the first to identify potential attack models that systems should be able to detect.

Locating Wire Short Fault for In-Vehicle Controller Area Network with Resistance Estimation Approach

Locating Wire Short Fault for In-Vehicle Controller Area Network with Resistance Estimation Approach

A Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN

Vehicle-IT convergence technology is a rapidly rising paradigm of modern vehicles, in which an electronic control unit (ECU) is used to control the vehicle electrical systems, and the controller area network (CAN), an in-vehicle network, is commonly used to construct an efficient network of ECUs. Unfortunately, security issues have not been treated properly in CAN, although CAN control messages could be life-critical. With the appearance of the connected car environment, in-vehicle networks (e.g., CAN) are now connected to external networks (e.g., 3G/4G mobile networks), enabling an adversary to perform a long-range wireless attack using CAN vulnerabilities. In this paper we show that a long-range wireless attack is physically possible using a real vehicle and malicious smartphone application in a connected car environment. We also propose a security protocol for CAN as a countermeasure designed in accordance with current CAN specifications. We evaluate the feasibility of the proposed security protocol using CANoe software and a DSP-F28335 microcontroller. Our results show that the proposed security protocol is more efficient than existing security protocols with respect to authentication delay and communication load.

Time-triggered controller area network

In the automotive industry vehicles are undergoing significant change, with much of the traditional mechanical control emphasis moving to distributed electronic systems. The article looks at an emerging time-triggered scheme, which is being retrofitted to the well known in-vehicle controller area network. This new time-triggered CAN will guarantee reliable scheduling of control messages within the automotive electronics environment.