Performance analysis of partial CAN based proposed algorithm

<div class="section abstract"><div class="htmlview paragraph">The amount of data communicated in automotive networks is increasing rapidly, leading to more complex systems alongside the rising number of Electronic Control Units (ECUs) in modern vehicles. As the number of ECUs has risen, Controller Area Network (CAN) communication bandwidth has been pushed to its limit. Existing solutions - such as using multiple CAN buses, sending multiple CAN frames or switching to another protocol - require a significant system-design effort and replacement of hardware and software. This has paved the way for CAN Flexible Data-rate (CAN FD), which extends the CAN standard and permits significantly higher data rates, enabling faster firmware upgrades. However, some changes must be made like: exchange the classical CAN microcontroller with a CAN FD microcontroller and also a CAN FD transceiver must be used.</div><div class="htmlview paragraph">This article will explore the benefits of CAN FD, how it compares with the classical CAN protocol, requirements for systems and how to introduce CAN FD into existing CAN networks.</div></div>

The response time distributions of the frames sent on a Controller Area Network (CAN) bus are of prime interest to dimension and validate automotive electronic architectures. However, the existing work on the timing behaviour of the CAN network does not take into account that all the data exchanges between the Electronic Control Units (ECUs) are driven by different and independent clocks which are subject to clock drifts. This paper proposes a model for clock drifts and describes their impact on the CAN frame response time distributions. By implementing the clock drifts in a CAN simulation tool, we show experimentally that the response time distributions converge, for drift values chosen randomly within the same range on all ECUs, whatever the initial phasings between the sending nodes. Furthermore, we show that, as a result of the clock drifts, the situations leading to the worst case response times are transient.

Modern cars are equipped with a wide variety of sensors generating continually growing amounts of data. This data is transmitted via bus systems such as Controller Area Network (CAN) inside of the vehicle to the microcontroller-based Electronic Control Units. By connecting the vehicle to its surroundings using wireless interfaces, this data becomes accessible to the vehicle manufacturer from a distance. Through the opening to the outside, cyber attacks can exploit these interfaces and introduce major risks to the privacy and safety of vehicle users. Hence, suitable methods for vehicle security monitoring such as intrusion detection and logging are needed. In this work, we focus on the logging of network data, since this data is useful for the development of security updates, countermeasures and incident signatures. On this account, we propose a new method to aggregate the data of the CAN bus. The method combines CAN frames into so-called flows. Each flow contains a set of packets that share a certain common attribute (e.g.: frame type and identifier). To integrate security monitoring of vehicle fleets seamlessly into backend server systems, the gathered CAN flow data is stored in an industry standard data format. Additionally, the payload data is included in the flow format using a compression algorithm to leverage deep-packet inspection. The evaluation results with realworld vehicle data indicate that in our case about 40 % reduction of the overall data size is possible with our method compared to industry-standard formats for storing CAN frames. On this account, we propose a new method to aggregate the data of the CAN bus. The method combines CAN frames into so-called flows. Each flow contains a set of packets that share a certain common attribute (e.g.: frame type and identifier). To integrate security monitoring of vehicle fleets seamlessly into backend server systems, the gathered CAN flow data is stored in an industry standard data format. Additionally, the payload data is included in the flow format using a compression algorithm to leverage deep-packet inspection. The evaluation results with realworld vehicle data indicate that in our case about 40 % reduction of the overall data size is possible with our method compared to industry-standard formats for storing CAN frames.

As de facto standards of in-vehicle network communications among various ECUs (Electronic Control Units), CAN (Controller Area Network) protocols invented by Bosch rely on the privately-defined unique identifiers CAN ID in CAN messages which do not convey any destination address. On the other hand, sticking to error handling mechanisms in CAN protocols, an ECU with an amount of transmission errors would enter bus-off state (i.e., go offline thereupon) and shall be recovered according to some prescribed bus-off recovery mode (BOM). All these sensitive CAN knowledge concealed inside an ECU by OEMs (Original Equipment Manufacturers) shall not be revealed and could be found extensive practical applications to both adversarial behavior (e.g., target ECU attacks) and security enhancement mechanisms (e.g., intrusion detection system designs). The paper presents FirmCAN, the first automatic analysis framework to dope out sensitive CAN knowledge (CAN IDs and bus-off recovery mode in particular) compiled in automotive ECU firmwares. FirmCAN first identifies base address (using accurate absolute function entry addresses), and then performs CAN module API positioning and sensitive configuration information resolution. We buckle down to automotive ECUs that resort to fixed-address mailboxes as CAN module transmission buffers (e.g., Renesas SuperH/RA series) and present concrete algorithms for each analysis phase. Our experimental evaluations first investigate firmwares extracted from real automotive ECUs. We then develop our own applications using RA6M4 development boards, which not only produce required firmwares to evaluate FirmCAN, but also create ground truth through hardware debugging. All evaluations demonstrate that FirmCAN can accurately garner above-mentioned sensitive CAN knowledge. FirmCAN can be trivially generalized to engage in extended frames and CAN modules with similar transmission logic, e.g., TI (Texas Instruments), ST (ST Microelectronics), etc.

Rec-CNN: In-vehicle networks intrusion detection using convolutional neural networks trained on recurrence plots

The auxiliary functions of modern vehicles have increased the number of attack surfaces. As the most important information exchange channel for vehicles, Controller Area Network (CAN) attracts more and more attention. Due to no encryption and authentication, CAN bus is vulnerable to attack. Thus, the attacker can manipulate the message with serious consequences. Several methods have been proposed to improve the security of vehicles. However, most of them have their own deficiencies. In this paper, we propose an LOF-based attack detection scheme which utilizes the voltage physical characteristics of the CAN frame to judge whether a message is transmitted by a legitimate Electronic Control Unit (ECU). As validated by extensive experiments on two vehicles, our scheme can identify the attacker with an average detection rate of 98.9\%, and the false detection rate is less than 0.5\%. In addition, our scheme can accurately identify attacks from external devices.

Abstract- Modern vehicles use the Electronics Control Unit (ECU) to control and monitor all the activities within the vehicle. The number of ECUs are increasing as the complexity of vehicles increases. All the ECUs present in the vehicles are communicated with each other via CAN protocol. Any malfunction in the ECU or abnormal behaviour of ECU is detected or understood by diagnostic services. CAN Protocol does not have advanced features like Diagnostic. The CAN protocol covers only the Physical and Data link layer of the OSI model. There is a need for a standardised diagnostic protocol which can use CAN as underlying technology. Standardised diagnostic protocols used in the automotive domain are On Board Diagnostics (OBD) and Unified Diagnostic services (UDS). UDS protocol is defined under the ISO 14229 standard and provides a standardized framework for in-vehicle communication and fault diagnosis. This project focuses on the design and implementation of the UDS protocol on an embedded system using STM32F407 microcontrollers. The project involves developing a diagnostic communication system between Electronic Control Units (ECUs) Light Control Module (LCM) and Tester tool connected over a CAN bus. Keywords- Unified Diagnostic Services (UDS), ISO 14229, Electronic Control Unit (ECU), Body Control Module (BCM), Light Control Module (LCM), Controller Area Network (CAN), Diagnostic Trouble Code (DTC), Routine Control Identifier (RID), Data Identifier (DID), ISO-TP (Transport Protocol)

<div class="section abstract"><div class="htmlview paragraph">In-vehicle communication among different vehicle electronic controller units (ECU) to run several applications (I.e. to propel the vehicle or In-vehicle Infotainment), CAN (Controller Area Network) is most frequently used. Given the proprietary nature and lack of standardization in CAN configurations, which are often not disclosed by manufacturers, the process of CAN reverse engineering becomes highly complex and cumbersome. Additionally, the scarcity of publicly accessible data on electric vehicles, coupled with the rapid technological advancements in this domain, has resulted in the absence of a standardized and automated methodology for reverse engineering the CAN. This process is further complicated by the diverse CAN configurations implemented by various Original Equipment Manufacturers (OEMs).</div><div class="htmlview paragraph">This paper presents a manual approach to reverse engineer the series CAN configuration of an electric vehicle, considering no vehicle information is available to testing engineers. To execute reverse engineering, the CAN data log is to be taken from the OBD-II port by systematically identifying and mapping the CAN with various ECUs interfaced with that CAN line. Driver actions and continuous data logged from the OBD-II port are cross-referenced with CAN data to determine the byte order of signals and message frames. The signals derived from one vehicle use scenario (driving) are then validated against another scenario (charging) to ensure consistency and accuracy.</div></div>

The controller area network (CAN) protocol is the most widely used in-vehicle network because it provides bounded transmission delay among electronic control units (ECUs) at data rates between 125Kbps and 1Mbps. And, many automotive companies have chosen the CAN protocol for their in-vehicle networking (IVN) system such as chassis network system of intelligent vehicles because of its excellent communication characteristics. However, the increasing number of ECUs and the need for more intelligent functions of advanced driver assistance systems (ADASs) or in-vehicle information systems (IVISs) require a network with more network capacity and real-time quality-of-service (QoS). As one approach to enhancing the network capacity of a CAN system, this paper introduces a CAN system with dual communication channel. And, this paper presents message allocation algorithm that allocates messages to the more appropriate channel using forecast traffic of each channel. An experimental testbed using commercial off-the-shelf microcontrollers with two CAN controllers was used to demonstrate the feasibility of the CAN system with dual communication channel using message allocation algorithm.

The most vehicle manufacturers have been using the CAN (Controller Area Network) in order to communicate among ECUs in vehicle systems for a long time. However, CAN has a disadvantage in that it cannot send and receive large amounts of data quickly due to its limited speed and short data transmission size. The CAN FD (Controller Area Network with Flexible Data rate) was developed to compensate for this disadvantage, and it has the advantage of being able to communicate at more fast speed and eight times the data transmission size compared to CAN. These CAN and CAN FD are vulnerable to message replay attacks because they do not provide data authentication. In this paper, secure communication is implemented by applying CMAC (Cipher based Message Authentication Code) based on CAN and CAN FD. Also, the performance of the two secure communications is compared and analyzed. To implement CMAC, HW-based AES-128 (Advanced Encryption Standard) CBC (Cipher Block Chaining) algorithm is used. This paper compares the performance of Secure CAN and CAN FD communication based on CMAC and can help to check communication traffic of ECU (Electronic Control Unit) in advance when applying the function in the real vehicle system.

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.

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.

Recent years have seen an exponential rise in complex software-driven functionality in vehicles, leading to a rising number of electronic control units (ECUs), network capabilities, and interfaces. These expanded capabilities also bring-in new planes of vulnerabilities making intrusion detection and management a critical capability; however, this can often result in more ECUs and network elements due to the high computational overheads. In this paper, we present a consolidated ECU architecture incorporating an Intrusion Detection System (IDS) for Automotive Controller Area Network (CAN) along with traditional ECU functionality on an off-the-shelf hybrid FPGA device, with near-zero overhead for the ECU functionality. We propose two quantised multi-layer perceptrons (QMLP's) as isolated IDSs for detecting a range of attack vectors including Denial-of-Service, Fuzzing and Spoofing, which are accelerated using off-the-shelf deep-learning processing unit (DPU) IP block from Xilinx, operating fully transparently to the software on the ECU. The proposed models achieve the state-of-the-art classification accuracy for all the attacks, while we observed a 15x reduction in power consumption when compared against the GPU-based implementation of the same models quantised using Nvidia libraries. We also achieved a 2.3x speed up in per-message processing latency (at 0.24 ms from the arrival of a CAN message) to meet the strict end-to-end latency on critical CAN nodes and a 2.6x reduction in power consumption for inference when compared to the state-of-the-art IDS models on embedded IDS and loosely coupled IDS accelerators (GPUs) discussed in the literature.

The Controller Area Network (CAN) is an established standard for inter-connecting onboard Electronic Control Units (ECUs) in a vehicle. Through sensors and actuators, ECUs maintain critical vehicle functions such as transmission and engine control. However, security was never a part of CAN design and hence ECUs are susceptible to a wide range of attacks. Thus, in recent years, several anomaly detection systems have been proposed for the CAN bus in order to detect anomalies caused by adversarial attacks or misbehaving sensors. These systems generally try to detect deviations from individual sensor's expected behavior. As such, they are ineffective against attacks that target multiple sensors to accomplish a collective desired behavior without changing the expected behavior of each individual sensor. In this paper, we focus on detecting such attacks by identifying contextual CAN anomalies in realtime. To this end, we present NeuroCAN, a deep learning-based detection system that utilizes Linear embeddings and Long Short Term Memory (LSTM) units to learn the spatio-temporal correlations among sensor data on the CAN bus at a frame level. By exploiting such correlations, NeuroCAN is able to detect contextual anomalies that are otherwise difficult to detect by analyzing individual sensor data. We evaluate NeuroCAN using two publicly available CAN datasets and compare it against existing approaches. Our results show that NeuroCAN achieves over 95% detection accuracy and performs significantly better than the existing baselines.

The dependability deficiencies and bandwidth constraints of the controller area network (CAN) can prevent its use in safety-relevant and performance-demanding applications. This paper introduces mechanisms for fault detection and fault isolation based on an intelligent CAN router, which exploits a priori knowledge about the permitted behavior of attached electronic control units (ECUs) in order to detect and contain failures. Experiments using an FPGA-based implementation of the CAN router evaluate these mechanisms under different failure modes (e.g., timing failures, masquerading failures). Due to its compatibility to the CAN standard, the router can improve the dependability and performance of systems with existing ECUs. In addition, we extend the application areas of CAN to systems with higher performance and dependability requirements than can be supported with a conventional bus-based network.