Performance Analysis for Secure Communication Based on CMAC

<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 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.

To realize the optimized design of the automotive cyber-physical system, it is required to consider the interplay between the communication and computation. However, the existing research about the design of the controller area network (CAN) with flexible data rate (CAN FD) ignores this, it only considers the minimization of the bandwidth utilization and neglects the fact that it would trigger too many unnecessary message receiving interrupts (MRIs) on message receiving electronic control units. To address this problem, this article formulates a tradeoff problem that balances the bandwidth utilization and the number of unnecessary MRIs during the design of the CAN FD. We first propose an algorithm to analyze the number of unnecessary MRIs triggered by the packed messages, and then, two heuristic algorithms, namely, the Top-Down approach and the Hybrid approach, are proposed to resolve the tradeoff problem for midsized and large signal sets, respectively. The experiment results show that compared with the state-of-the-art algorithm, the Top-Down approach reduces the unnecessary MRIs by 10.48%-99.89% with only 0.02%-1.32% bandwidth utilization overhead, the Hybrid approach reduces the unnecessary MRIs by 23.15%-99.63% with only 0.13%-2.07% bandwidth utilization overhead.

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.

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 Partial controller area network (CAN), which can partially turn on and off the operation of electronic control unit (ECU), is a low-power technology and can reduce the CO2 emissions and power consumption of an automobile compared with the existing automobile CAN. However, a problem arises in the application of the Partial CAN whereby the existing automobile CAN databases that contain all the CAN frames of the automobile have many period frames. The periodic frame wakes the ECU every constant cycle, so the power consumption and CO2 emissions savings of ECU are ineffective. To solve this problem, we proposed a new algorithm for applying the Partial CAN that can evade the period wake-up of the ECU. In this paper, we introduce a new algorithm for applying the Partial CAN and analyze the performance of the proposed algorithm by comparing with the existing algorithm.

<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 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.

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)

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%.

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.

Intravehicular communication relies on controller area network (CAN) protocol to deliver messages and instructions among different electronic control units (ECU). Unfortunately, inherent defects in CAN include the absence of confidentiality and integrity mechanism, enabling adversaries to launch attacks from wired or wireless interfaces. Although various CAN cryptographic protocols have been proposed for entity authentication and secure communication, the redundancy in the key establishment phase weakens their availability in large-scale CAN. In this paper, we propose a scalable security protocol suite for intravehicular networks and reduce the communication costs significantly. A new type of attack, suspension attack, is identified for the existing protocols and mitigated in our protocol by leveraging a global counter scheme. We formally verify the security properties of the proposed protocol suite through the AVISPA tool. The simulation results indicate that the communication and computation efficiency are improved in our protocol.

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.

Study and Prototype Implementation of Basic Non-CAN and CAN Based Communication in Context with Automotive Application(s)