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The progress in machine learning has led to advanced deep neural networks. These networks are widely used in computer vision tasks and safety-critical applications. The automotive industry, in particular, has experienced a significant transformation with the integration of deep learning techniques and neural networks. This integration contributes to the realization of autonomous driving systems. Object detection is a crucial element in autonomous driving. It contributes to vehicular safety and operational efficiency. This technology allows vehicles to perceive and identify their surroundings. It detects objects like pedestrians, vehicles, road signs, and obstacles. Object detection has evolved from being a conceptual necessity to an integral part of advanced driver assistance systems (ADAS) and the foundation of autonomous driving technologies. These advancements enable vehicles to make real-time decisions based on their understanding of the environment, improving safety and driving experiences. However, the increasing reliance on deep neural networks for object detection and autonomous driving has brought attention to potential vulnerabilities within these systems. Recent research has highlighted the susceptibility of these systems to adversarial attacks. Adversarial attacks are well-designed inputs that exploit weaknesses in the deep learning models underlying object detection. Successful attacks can cause misclassifications and critical errors, posing a significant threat to the functionality and safety of autonomous vehicles. With the rapid development of object detection systems, the vulnerability to adversarial attacks has become a major concern. These attacks manipulate inputs to deceive the target system, significantly compromising the reliability and safety of autonomous vehicles. In this study, we focus on analyzing adversarial attacks on state-of-the-art object detection models. We create adversarial examples to test the models’ robustness. We also check if the attacks work on a different object detection model meant for similar tasks. Additionally, we extensively evaluate recent defense mechanisms to see how effective they are in protecting deep neural networks (DNNs) from adversarial attacks and provide a comprehensive overview of the most commonly used defense strategies against adversarial attacks, highlighting how they can be implemented practically in real-world situations.
As cyber threats continue to evolve, it is becoming increasingly important for organizations to have a Security Operations Center (SOC) in place to effectively defend against them. However, building and maintaining a SOC can be a daunting task without clear guidelines, policies, and procedures in place. Additionally, most current SOC solutions used by organizations are outdated, lack key features and integrations, and are expensive to maintain and upgrade. Moreover, proprietary solutions can lead to vendor lock-in, making it difficult to switch to a different solution in the future.
To address these challenges, this thesis proposes a comprehensive SOC framework and an open-source SOC solution that provides organizations with a flexible and cost-effective way to defend against modern cyber threats. The research methodology involved conducting a thorough literature review of existing literature and research on building and maintaining a SOC, including using SOC as a service. The data collected from the literature review was analyzed to identify common themes, challenges, and best practices for building and maintaining a SOC.
Based on the data collected, a comprehensive framework for building and maintaining a SOC was developed. The framework addresses essential areas such as the scope and purpose of the SOC, governance and leadership, staffing and skills, technologies and tools, processes and procedures, service level agreements (SLAs), and evaluation and measurement. This framework provides organizations with the necessary guidance and resources to establish and effectively operate a SOC, as well as a reference for evaluating the service provided by SOC service providers.
In addition to the SOC framework, a modern open-source SOC solution was developed, which emphasizes several key measures to help organizations defend against modern cyber threats. These measures include real-time, actionable threat intelligence, rapid and effective incident response, continuous security monitoring and alerting, automation, integration, and customization. The use of open-source technologies and a modular architecture makes the solution cost-effective, allowing organizations to scale it up or down as needed.
Overall, the proposed SOC framework and open-source SOC solution provide organizations with a comprehensive and systematic approach for building and maintaining a SOC that is aligned with the needs and objectives of the organization. The open-source SOC solution provides a flexible and cost-effective way to defend against modern cyber threats, helping organizations to effectively operate their SOC and reduce their risk of security incidents and breaches.
KI-gestützte Cyberangriffe
(2023)
Die Fortschritte im Bereich der künstlichen Intelligenz (KI) und des Deep Learning haben in den letzten Jahren enorme Fortschritte gemacht. Insbesondere Technologien wie Large Language Models (LLMs) machen KI-Technologie innerhalb kurzer Zeit zugänglich für die Allgemeinheit. Die Generierung von Text, Bild und Sprache durch künstliche Intelligenz erzielt innerhalb kurzer Zeit gute Ergebnisse. Parallel zu dieser Entwicklung hat die Cyberkriminalität in den vergangenen Jahren im Bereich der KI zugenommen. Cyberangriffe verursachen im Zuge der Digitalisierung größeren Schaden und Angriffe entwickeln sich kontinuierlich weiter, um bestehende Schutzmaßnahmen zu umgehen.
Diese Arbeit bietet eine Einführung in das Themengebiet KI-gestützte Cyberangriffe. Sie präsentiert aktuelle KI-gestützte Cyberangriffsmodelle und analysiert, inwiefern diese für Anfänger*innen in der Cyberkriminalität zugänglich sind.
In modernen Industrieautomatisierungssysteme kann die IT-Sicherheit nicht mehr ignoriert werden. Um dem Datenverkehr Schutz zu bieten, sind kryptografische Schutzmaßnahmen notwendig. Eine gängige Schutzmaßnahme ist die Verwendung von digitalen Zertifikaten zur Autorisierung und Authentifizierung. Um Zertifikate sicher und geregelt auf Endgeräte zu bringen, ist jedoch eine Public-Key-Infrastructure notwendig. Solche PKIs sind bisher wenig im Umfeld der Industrieautomatisierung untersucht. Das Institut für verlässliche Embedded-Systems der Hochschule Offenburg bietet hierfür eine mögliche Lösung, welche auf einer zentralen Einheit, genannt Credentialing Entity, basiert. Ein Demonstrator dieses Konzepts wurde bereits in den weit verbreiteten Systemprogrammier-sprachen C und C++ implementiert.
Im Rahmen dieser Arbeit wird die Verwendung der modernen speichersicheren Programmiersprache Rust in der Systemprogrammierung als Alternative zu den Domänenführern C/C++ am Beispiel der Implementierung der Credentialing-Entity untersucht. Hierbei werden Aspekte wie die Vorzüge Rusts, dessen Ökosystem und Interoperabilität mit den Marktführern C/C++ untersucht.
Extensible Authentication Protocol (EAP) bietet eine flexible Möglichkeit zur Authentifizierung von Endgeräten und kann in Kombination mit TLS für eine zertifikatsbasierte Authentifizierung verwendet werden. Motiviert wird diese Arbeit von einer potenziellen Erweiterung für PROFINET, die diese Protokolle einsetzen soll.
Dabei soll eine sicherer EAP-TLS-Protokollstacks für eingebettete Systeme in der Programmiersprache Rust entwickelt werden. Durch das Ownership-System von Rust können Speicherfehler eliminiert werden, ohne dabei auf die positiven Eigenschaften von nativen Sprachen zu verzichten. Es wird ein besonderes Augenmerk auf wie die Verwendung klassischer Rust-Bibliotheken im Umfeld von eingebetteten Systemen, den Einfluss des Speichermodells auf das Design, sowie die Integration von C-Bibliotheken für automatisierte Interoperabilitätstests gelegt.