A survey on online monitoring approaches of computer-based systems

This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use

Anomaly detection with machine learning for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.Today's automotive systems are evolving at a rapid pace and there has been a seismic shift in automotive technology in the past few years. Automakers are racing to redefine the automobile as a fully autonomous and connected system. As a result, new technologies such as advanced driver assistance systems (ADAS), vehicle-to-vehicle (V2V), 5G vehicle to infrastructure (V2I), and vehicle to everything (V2X), etc. have emerged in recent years. These advances have resulted in increased responsibilities for the electronic control units (ECUs) in the vehicles, requiring a more sophisticated in-vehicle network to address the growing communication needs of ECUs with each other and external subsystems. This in turn has transformed modern vehicles into a complex distributed cyber-physical system. The ever-growing connectivity to external systems in such vehicles is introducing new challenges, related to the increasing vulnerability of such vehicles to various cyber-attacks. A malicious actor can use various access points in a vehicle, e.g., Bluetooth and USB ports, telematic systems, and OBD-II ports, to gain unauthorized access to the in-vehicle network. These access points are used to gain access to the network from the vehicle's attack surface. After gaining access to the in-vehicle network through an attack surface, a malicious actor can inject or alter messages on the network to try to take control of the vehicle. Traditional security mechanisms such as firewalls only detect simple attacks as they do not have the ability to detect more complex attacks. With the increasing complexity of vehicles, the attack surface increases, paving the way for more complex and novel attacks in the future. Thus, there is a need for an advanced attack detection solution that can actively monitor the in-vehicle network and detect complex cyber-attacks. One of the many approaches to achieve this is by using an intrusion detection system (IDS). Many state-of-the-art IDS employ machine learning algorithms to detect cyber-attacks for its ability to detect both previously observed as well as novel attack patterns. Moreover, the large availability of in-vehicle network data and increasing computational power of the ECUs to handle emerging complex automotive tasks facilitates the use of machine learning models. Therefore, due to its large spectrum of attack coverage and ability to detect complex attack patterns, we adopt and propose two novel machine learning based IDS frameworks (LATTE and TENET) for in-vehicle network anomaly detection. Our proposed LATTE framework uses sequence models, such as LSTMs, in an unsupervised setting to learn the normal system behavior. LATTE leverages the learned information at runtime to detect anomalies by observing for any deviations from the learned normal behavior. Our proposed LATTE framework aims to maximize the anomaly detection accuracy, precision, and recall while minimizing the false-positive rate. The increased complexity of automotive systems has resulted in very long term dependencies between messages which cannot be effectively captured by LSTMs. Hence to overcome this problem, we proposed a novel IDS framework called TENET. TENET employs a novel convolutional neural attention (TCNA) based architecture to effectively learn very-long term dependencies between messages in an in-vehicle network during the training phase and leverage the learned information in combination with a decision tree classifier to detect anomalous messages. Our work aims to efficiently detect a multitude of attacks in the in-vehicle network with low memory and computational overhead on the ECU

Ein mehrschichtiges sicheres Framework für Fahrzeugsysteme

In recent years, significant developments were introduced within the vehicular domain, evolving the vehicles to become a network of many embedded systems distributed throughout the car, known as Electronic Control Units (ECUs). Each one of these ECUs runs a number of software components that collaborate with each other to perform various vehicle functions. Modern vehicles are also equipped with wireless communication technologies, such as WiFi, Bluetooth, and so on, giving them the capability to interact with other vehicles and roadside infrastructure. While these improvements have increased the safety of the automotive system, they have vastly expanded the attack surface of the vehicle and opened the door for new potential security risks. The situation is made worse by a lack of security mechanisms in the vehicular system which allows the escalation of a compromise in one of the non-critical sub-systems to threaten the safety of the entire vehicle and its passengers. This dissertation focuses on providing a comprehensive framework that ensures the security of the vehicular system during its whole life-cycle. This framework aims to prevent the cyber-attacks against different components by ensuring secure communications among them. Furthermore, it aims to detect attacks which were not prevented successfully, and finally, to respond to these attacks properly to ensure a high degree of safety and stability of the system.In den letzten Jahren wurden bedeutende Entwicklungen im Bereich der Fahrzeuge vorgestellt, die die Fahrzeuge zu einem Netzwerk mit vielen im gesamten Fahrzeug verteile integrierte Systeme weiterentwickelten, den sogenannten Steuergeräten (ECU, englisch = Electronic Control Units). Jedes dieser Steuergeräte betreibt eine Reihe von Softwarekomponenten, die bei der Ausführung verschiedener Fahrzeugfunktionen zusammenarbeiten. Moderne Fahrzeuge sind auch mit drahtlosen Kommunikationstechnologien wie WiFi, Bluetooth usw. ausgestattet, die ihnen die Möglichkeit geben, mit anderen Fahrzeugen und der straßenseitigen Infrastruktur zu interagieren. Während diese Verbesserungen die Sicherheit des Fahrzeugsystems erhöht haben, haben sie die Angriffsfläche des Fahrzeugs erheblich vergrößert und die Tür für neue potenzielle Sicherheitsrisiken geöffnet. Die Situation wird durch einen Mangel an Sicherheitsmechanismen im Fahrzeugsystem verschärft, die es ermöglichen, dass ein Kompromiss in einem der unkritischen Subsysteme die Sicherheit des gesamten Fahrzeugs und seiner Insassen gefährdet kann. Diese Dissertation konzentriert sich auf die Entwicklung eines umfassenden Rahmens, der die Sicherheit des Fahrzeugsystems während seines gesamten Lebenszyklus gewährleistet. Dieser Rahmen zielt darauf ab, die Cyber-Angriffe gegen verschiedene Komponenten zu verhindern, indem eine sichere Kommunikation zwischen ihnen gewährleistet wird. Darüber hinaus zielt es darauf ab, Angriffe zu erkennen, die nicht erfolgreich verhindert wurden, und schließlich auf diese Angriffe angemessen zu reagieren, um ein hohes Maß an Sicherheit und Stabilität des Systems zu gewährleisten

Developing and Deploying Security Applications for In-Vehicle Networks

Radiological material transportation is primarily facilitated by heavy-duty on-road vehicles. Modern vehicles have dozens of electronic control units or ECUs, which are small, embedded computers that communicate with sensors and each other for vehicle functionality. ECUs use a standardized network architecture--Controller Area Network or CAN--which presents grave security concerns that have been exploited by researchers and hackers alike. For instance, ECUs can be impersonated by adversaries who have infiltrated an automotive CAN and disable or invoke unintended vehicle functions such as brakes, acceleration, or safety mechanisms. Further, the quality of security approaches varies wildly between manufacturers. Thus, research and development of after-market security solutions have grown remarkably in recent years. Many researchers are exploring deployable intrusion detection and prevention mechanisms using machine learning and data science techniques. However, there is a gap between developing security system algorithms and deploying prototype security appliances in-vehicle. In this paper, we, a research team at Oak Ridge National Laboratory working in this space, highlight challenges in the development pipeline, and provide techniques to standardize methodology and overcome technological hurdles.Comment: 10 pages, PATRAM 2

A Study of Automotive Security : CAN Bus Intrusion detection Systems, Attack Surface, and Regulations

The innovation in the automotive sector enhanced the technology implemented in vehicles by the manufacturers. Consequently, the overall driving experience improved, thanks to the introduction of better safety, utility, and entertainment systems. Moreover, automobiles began collecting and exchanging data with the external world through different communication protocols. However, these additions have started to attract attention from security experts. More importantly, malevolent attackers have exploited the technologies and their related attack points to carry out malicious activities to cause data security and safety issues. These issues have led to establishing standards and regulations (ISO 21434, UNECE 155, etc.) that redefine vehicle design and development by incorporating security protocols and requirements necessary to create secure automobiles. However, these documents analyze the problem at a high level and do not dwell on practical solutions implementation analysis. This work presents an in-depth study of in-vehicle communication concerns via Controller Area Network (CAN) bus safety problems analysis with different proposed solutions. Specifically, a survey of Intrusion Detection Systems developed in the literature is brought up: simulation of three CAN bus intrusion detection systems against various attacks. The results show effectiveness against disruptive attacks, i.e., with numerous messages sent in a short period of time, but conversely have difficulty detecting more targeted attacks with few transmitted packets. The solutions analysis is an excellent starting point for security engineers to be able to develop Intrusion Detection Systems for the CAN bus capable of detecting attacks that will become increasingly complex and difficult to counter over time

An Overview of Automotive Service-Oriented Architectures and Implications for Security Countermeasures

New requirements from the customers\u27 and manufacturers\u27 point of view such as adding new software functions during the product life cycle require a transformed architecture design for future vehicles. The paradigm of signal-oriented communication established for many years will increasingly be replaced by service-oriented approaches in order to increase the update and upgrade capability. In this article, we provide an overview of current protocols and communication patterns for automotive architectures based on the service-oriented architecture (SOA) paradigm and compare them with signal-oriented approaches. Resulting challenges and opportunities of SOAs with respect to information security are outlined and discussed. For this purpose, we explain different security countermeasures and present a state of the section of automotive approaches in the fields of firewalls, Intrusion Detection Systems (IDSs) and Identity and Access Management (IAM). Our final discussion is based on an exemplary hybrid architecture (signal- and service-oriented) and examines the adaptation of existing security measures as well as their specific security features