From Attack to Defense: Toward Secure In-vehicle Networks

New security breaches in vehicles are emerging due to software-driven Electronic Control Units (ECUs) and wireless connectivity of modern vehicles. These trends have introduced more remote surfaces/endpoints that an adversary can exploit and, in the worst case, use to control the vehicle remotely. Researchers have demonstrated how vulnerabilities in remote endpoints can be exploited to compromise ECUs, access in-vehicle networks, and control vehicle maneuvers. To detect and prevent such vehicle cyber attacks, researchers have also developed and proposed numerous countermeasures (e.g., Intrusion Detection Systems and message authentication schemes). However, there still remain potentially critical attacks that existing defense schemes can neither detect/prevent nor consider. Moreover, existing defense schemes lack certain functionalities (e.g., identifying the message transmitter), thus not providing strong protection for safety-critical ECUs against in-vehicle network attacks. With all such unexplored and unresolved security issues, vehicles and drivers/passengers will remain insecure. This dissertation aims to fill this gap by 1) unveiling a new important and critical vulnerability applicable to several in-vehicle networks (including the Controller Area Network (CAN), the de-facto standard protocol), 2) proposing a new Intrusion Detection System (IDS) which can detect not only those attacks that have already been demonstrated or discussed in literature, but also those that are more acute and cannot be detected by state-of-the-art IDSes, 3) designing an attacker identification scheme that provides a swift pathway for forensic, isolation, security patch, etc., and 4) investigating what an adversary can achieve while the vehicle’s ignition is off. First, we unveil a new type of Denial-of-Service (DoS) attack called the bus-off attack that, ironically, exploits the error-handling scheme of in-vehicle networks. That is, their fault-confinement mechanism — which has been considered as one of their major advantages in providing fault-tolerance and robustness — is used as an attack vector. Next, we propose a new anomaly-based IDS that detects intrusions based on the extracted fingerprints of ECUs. Such a capability overcomes the deficiency of existing IDSes and thus detects a wide range of in-vehicle network attacks, including those existing schemes cannot. Then, we propose an attacker identification scheme that provides a swift pathway for forensic, isolation, and security patch. This is achieved by fingerprinting ECUs based on CAN voltage measurements. It takes advantage of the fact that voltage outputs of each ECU are slightly different from each other due to their differences in supply voltage, ground voltage, resistance values, etc. Lastly, we propose two new attack methods called the Battery-Drain and the Denial-of-Body-control attacks through which an adversary can disable parked vehicles with the ignition off. These attacks invalidate the conventional belief that vehicle cyber attacks are feasible and thus their defenses are required only when the vehicles ignition is on.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144125/1/ktcho_1.pd

A robust, reliable and deployable framework for In-vehicle security

Cyber attacks on financial and government institutions, critical infrastructure, voting systems, businesses, modern vehicles, etc., are on the rise. Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. This is due to the fact that the protocols used for in-vehicle communication i.e. controller area network (CAN), FlexRay, local interconnect network (LIN), etc., lack basic security features such as message authentication, which makes it vulnerable to a wide range of attacks including spoofing attacks. This research presents methods to protect the vehicle against spoofing attacks. The proposed methods exploit uniqueness in the electronic control unit electronic control unit (ECU) and the physical channel between transmitting and destination nodes for linking the received packet to the source. Impurities in the digital device, physical channel, imperfections in design, material, and length of the channel contribute to the uniqueness of artifacts. I propose novel techniques for electronic control unit (ECU) identification in this research to address security vulnerabilities of the in-vehicle communication. The reliable ECU identification has the potential to prevent spoofing attacks launched over the CAN due to the inconsideration of the message authentication. In this regard, my techniques models the ECU-specific random distortion caused by the imperfections in digital-to-analog converter digital to analog converter (DAC), and semiconductor impurities in the transmitting ECU for fingerprinting. I also model the channel-specific random distortion, impurities in the physical channel, imperfections in design, material, and length of the channel are contributing factors behind physically unclonable artifacts. The lumped element model is used to characterize channel-specific distortions. This research exploits the distortion of the device (ECU) and distortion due to the channel to identify the transmitter and hence authenticate the transmitter.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154568/1/Azeem Hafeez Final Disseration.pdfDescription of Azeem Hafeez Final Disseration.pdf : Dissertatio

vProfile: Voltage-Based Sender Identification on Controller Area Networks

Modern vehicles are becoming more accessible targets for cyberattacks due to the proliferation of wireless communication channels. The intra-vehicle Controller Area Network (CAN) bus lacks sender authentication, exposing critical components to interference from less secure, wirelessly compromised modules. To address CAN's vulnerability, this thesis proposes vProfile, a sender identification system based on voltage fingerprints of electronic control units (ECU). vProfile exploits the physical properties of ECU output voltages on the CAN bus to determine the authenticity of bus messages, which enables the detection of both hijacked ECUs and external devices connected to the bus. We show the potential of vProfile using experiments on two production vehicles with precision and recall scores of over 99.99%. We also show the impact of temperature and battery voltage variations on vProfile and provide a method to adapt to those changes. The improved identification rates and more straightforward design of vProfile make it an attractive improvement over existing methods

Energy Harvesting and Sensor Based Hardware Security Primitives for Cyber-Physical Systems

The last few decades have seen a large proliferation in the prevalence of cyber-physical systems. Although cyber-physical systems can offer numerous advantages to society, their large scale adoption does not come without risks. Internet of Things (IoT) devices can be considered a significant component within cyber-physical systems. They can provide network communication in addition to controlling the various sensors and actuators that exist within the larger cyber-physical system. The adoption of IoT features can also provide attackers with new potential avenues to access and exploit a system\u27s vulnerabilities. Previously, existing systems could more or less be considered a closed system with few potential points of access for attackers. Security was thus not typically a core consideration when these systems were originally designed. The cumulative effect is that these systems are now vulnerable to new security risks without having native security countermeasures that can easily address these vulnerabilities. Even just adding standard security features to these systems is itself not a simple task. The devices that make up these systems tend to have strict resource constraints in the form of power consumption and processing power. In this dissertation, we explore how security devices known as Physically Unclonable Functions (PUFs) could be used to address these concerns. PUFs are a class of circuits that are unique and unclonable due to inherent variations caused by the device manufacturing process. We can take advantage of these PUF properties by using the outputs of PUFs to generate secret keys or pseudonyms that are similarly unique and unclonable. Existing PUF designs are commonly based around transistor level variations in a special purpose integrated circuit (IC). Integrating these designs within a system would still require additional hardware along with system modification to interact with the device. We address these concerns by proposing a novel PUF design methodology for the creation of PUFs whose integration within these systems would minimize the cost of redesigning the system by reducing the need to add additional hardware. This goal is achieved by creating PUF designs from components that may already exist within these systems. A PUF designed from existing components creates the possibility of adding a PUF (and thus security features) to the system without actually adding any additional hardware. This could allow PUFs to become a more attractive security option for integration with resource constrained devices. Our proposed approach specifically targets sensors and energy harvesting devices since they can provide core functions within cyber-physical systems such as power generation and sensing capabilities. These components are known to exhibit variations due to the manufacturing process and could thus be utilized to design a PUF. Our first contribution is the proposal of a novel PUF design methodology based on using components which are already commonly found within cyber-physical systems. The proposed methodology uses eight sensors or energy harvesting devices along with a microcontroller. It is unlikely that single type of sensor or energy harvester will exist in all possible cyber-physical systems. Therefore, it is important to create a range of designs in order to reach a greater portion of cyber-physical systems. The second contribution of this work is the design of a PUF based on piezo sensors. Our third contribution is the design of a PUF that utilizes thermistor temperature sensors. The fourth contribution of this work is a proposed solar cell based PUF design. Furthermore, as a fifth contribution of this dissertation we evaluate a selection of common solar cell materials to establish which type of solar cell would be best suited to the creation of a PUF based on the operating conditions. The viability of the proposed designs is evaluated through testing in terms of reliability and uniformity. In addition, Monte Carlo simulations are performed to evaluate the uniqueness property of the designs. For our final contribution we illustrate the security benefits that can be achieved through the adoption of PUFs by cyber-physical systems. For this purpose we chose to highlight vehicles since they are a very popular example of a cyber-physical system and they face unique security challenges which are not readily solvable by standard solutions. Our contribution is the proposal of a novel controller area network (CAN) security framework that is based on PUFs. The framework does not require any changes to the underlying CAN protocol and also minimizes the amount of additional message passing overhead needed for its operation. The proposed framework is a good example of how the cost associated with implementing such a framework could be further reduced through the adoption of our proposed PUF designs. The end result is a method which could introduce security to an inherently insecure system while also making its integration as seamless as possible by attempting to minimize the need for additional hardware

The Resilience of Deep Learning Intrusion Detection Systems for Automotive Networks

This thesis will cover the topic of cyber security in vehicles. Current vehicles contain many computers which communicate over a controller area network. This network has many vulnerabilities which can be leveraged by attackers. To combat these attackers, intrusion detection systems have been implemented. The latest research has mostly focused on the use of deep learning techniques for these intrusion detection systems. However, these deep learning techniques are not foolproof and possess their own security vulnerabilities. One such vulnerability comes in the form of adversarial samples. These are attacks that are manipulated to evade detection by these intrusion detection systems. In this thesis, the aim is to show that the known vulnerabilities of deep learning techniques are also present in the current state-of-the-art intrusion detection systems. The presence of these vulnerabilities shows that these deep learning based systems are still to immature to be deployed in actual vehicles. Since if an attacker is able to use these weaknesses to circumvent the intrusion detection system, they can still control many parts of the vehicles such as the windows, the brakes and even the engine. Current research regarding deep learning weaknesses has mainly focused on the image recognition domain. Relatively little research has investigated the influence of these weaknesses for intrusion detection, especially on vehicle networks. To show these weaknesses, firstly two baseline deep learning intrusion detection systems were created. Additionally, two state-of-the-art systems from recent research papers were recreated. Afterwards, adversarial samples were generated using the fast gradient-sign method on one of the baseline systems. These adversarial samples were then used to show the drop in performance of all systems. The thesis shows that the adversarial samples negatively impact the two baseline models and one state-of-the-art model. The state-of-the-art model’s drop in performance goes as high as 60% in the f1-score. Additionally, some of the adversarial samples need as little as 2 bits to be changed in order to evade the intrusion detection systems

Robust and secure resource management for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.Modern vehicles are examples of complex cyber-physical systems with tens to hundreds of interconnected Electronic Control Units (ECUs) that manage various vehicular subsystems. With the shift towards autonomous driving, emerging vehicles are being characterized by an increase in the number of hardware ECUs, greater complexity of applications (software), and more sophisticated in-vehicle networks. These advances have resulted in numerous challenges that impact the reliability, security, and real-time performance of these emerging automotive systems. Some of the challenges include coping with computation and communication uncertainties (e.g., jitter), developing robust control software, detecting cyber-attacks, ensuring data integrity, and enabling confidentiality during communication. However, solutions to overcome these challenges incur additional overhead, which can catastrophically delay the execution of real-time automotive tasks and message transfers. Hence, there is a need for a holistic approach to a system-level solution for resource management in automotive cyber-physical systems that enables robust and secure automotive system design while satisfying a diverse set of system-wide constraints. ECUs in vehicles today run a variety of automotive applications ranging from simple vehicle window control to highly complex Advanced Driver Assistance System (ADAS) applications. The aggressive attempts of automakers to make vehicles fully autonomous have increased the complexity and data rate requirements of applications and further led to the adoption of advanced artificial intelligence (AI) based techniques for improved perception and control. Additionally, modern vehicles are becoming increasingly connected with various external systems to realize more robust vehicle autonomy. These paradigm shifts have resulted in significant overheads in resource constrained ECUs and increased the complexity of the overall automotive system (including heterogeneous ECUs, network architectures, communication protocols, and applications), which has severe performance and safety implications on modern vehicles. The increased complexity of automotive systems introduces several computation and communication uncertainties in automotive subsystems that can cause delays in applications and messages, resulting in missed real-time deadlines. Missing deadlines for safety-critical automotive applications can be catastrophic, and this problem will be further aggravated in the case of future autonomous vehicles. Additionally, due to the harsh operating conditions (such as high temperatures, vibrations, and electromagnetic interference (EMI)) of automotive embedded systems, there is a significant risk to the integrity of the data that is exchanged between ECUs which can lead to faulty vehicle control. These challenges demand a more reliable design of automotive systems that is resilient to uncertainties and supports data integrity goals. Additionally, the increased connectivity of modern vehicles has made them highly vulnerable to various kinds of sophisticated security attacks. Hence, it is also vital to ensure the security of automotive systems, and it will become crucial as connected and autonomous vehicles become more ubiquitous. However, imposing security mechanisms on the resource constrained automotive systems can result in additional computation and communication overhead, potentially leading to further missed deadlines. Therefore, it is crucial to design techniques that incur very minimal overhead (lightweight) when trying to achieve the above-mentioned goals and ensure the real-time performance of the system. We address these issues by designing a holistic resource management framework called ROSETTA that enables robust and secure automotive cyber-physical system design while satisfying a diverse set of constraints related to reliability, security, real-time performance, and energy consumption. To achieve reliability goals, we have developed several techniques for reliability-aware scheduling and multi-level monitoring of signal integrity. To achieve security objectives, we have proposed a lightweight security framework that provides confidentiality and authenticity while meeting both security and real-time constraints. We have also introduced multiple deep learning based intrusion detection systems (IDS) to monitor and detect cyber-attacks in the in-vehicle network. Lastly, we have introduced novel techniques for jitter management and security management and deployed lightweight IDSs on resource constrained automotive ECUs while ensuring the real-time performance of the automotive systems