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PublicationBreaking TrustZone memory isolation and secure boot through malicious hardware on a modern FPGA-SoC( 2022)FPGA-SoCs are heterogeneous embedded computing platforms consisting of reconfigurable hardware and high-performance processing units. This combination offers flexibility and good performance for the design of embedded systems. However, allowing the sharing of resources between an FPGA and an embedded CPU enables possible attacks from one system on the other. This work demonstrates that a malicious hardware block contained inside the reconfigurable logic can manipulate the memory and peripherals of the CPU. Previous works have already considered direct memory access attacks from malicious logic on platforms containing no memory isolation mechanism. In this work, such attacks are investigated on a modern platform which contains state-of-the-art memory and peripherals isolation mechanisms. We demonstrate two attacks capable of compromising a Trusted Execution Environment based on ARM TrustZone and show a new attack capable of bypassing the secure boot configuration set by a device owner via the manipulation of Battery-Backed RAM and eFuses from malicious logic.
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PublicationSovereignly Donating Medical Data as a Patient: A Technical Approach( 2022)Data is the new asset of the 21st century, and many new business models are based on data. However, data is also needed in the medical research domain, such as in the procedure of applying new machine learning methods for gaining new medical findings. Furthermore, the hurdle arises that medical data comprises personal data, and thus, it requires particular care and protection. Hence, patients must consent to the data donation process for general medical research but without selecting specific research projects. We argue that patients must gain more influence in the data donation process to cover this lack of data sovereignty. Therefore, we developed a concept and implementation empowering patients to make sovereign decisions about donating their medical data to specific medical research projects. Our work comprises concepts of the Medical Informatics Initiative, International Data Spaces, and MY DATA Control Technologies with new specific elements combining these components. This approach of patient empowerment enables a new kind of data sovereignty in the medical research domain.
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PublicationMobile Contactless Fingerprint Recognition: Implementation, Performance and Usability Aspects( 2022)This work presents an automated contactless fingerprint recognition system for smart-phones. We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system. In addition, our implementation is made publicly available for research purposes. During a database acquisition, a total number of 1360 contactless and contact-based samples of 29 subjects are captured in two different environmental situations. Experiments on the acquired database show a comparable performance of our contactless scheme and the contact-based baseline scheme under constrained environmental influences. A comparative usability study on both capturing device types indicates that the majority of subjects prefer the contactless capturing method. Based on our experimental results, we analyze the impact of the current COVID-19 pandemic on fingerprint recognition systems. Finally, implementation aspects of contactless fingerp rint recognition are summarized.
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PublicationA Systematic Review on Model Watermarking for Neural Networks( 2021)Machine learning (ML) models are applied in an increasing variety of domains. The availability of large amounts of data and computational resources encourages the development of ever more complex and valuable models. These models are considered the intellectual property of the legitimate parties who have trained them, which makes their protection against stealing, illegitimate redistribution, and unauthorized application an urgent need. Digital watermarking presents a strong mechanism for marking model ownership and, thereby, offers protection against those threats. This work presents a taxonomy identifying and analyzing different classes of watermarking schemes for ML models. It introduces a unified threat model to allow structured reasoning on and comparison of the effectiveness of watermarking methods in different scenarios. Furthermore, it systematizes desired security requirements and attacks against ML model watermarking. Based on that framework, representative literature from the field is surveyed to illustrate the taxonomy. Finally, shortcomings and general limitations of existing approaches are discussed, and an outlook on future research directions is given.
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PublicationSecurity Concept with Distributed Trust-Levels for Autonomous Cooperative Vehicle Networks( 2021)The newly proposed cooperative intelligent transportation system (cITS) is a big step towards completely autonomous driving. It is a key requirement for vehicles to exchange crucial information. Only with exchanged data, such as hazard warnings or route planning each vehicle will have enough information to find its way without a driver. However, this data has to be authentic and trustworthy, since it will directly influence the behavior of every vehicle inside such a network. For authentic messages, public key infrastructure (PKI)-based asymmetric cryptography mechanisms were already proposed by different organizations, such as the European Telecommunications Standards Institute (ETSI). The second crucial information of trustworthiness is still missing. In this paper, a new security concept is presented, which introduces a trust-level for each vehicle to enable an assessment, whether data is trustworthy or not. Besides, a Pretty Good Privacy (PGP)-inspired certificate administration is proposed to manage the certificates and their affiliated trust-level. The new concept mitigates sybil attacks and increases the speed of data processing inside vehicles.
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PublicationVIA: Analyzing Device Interfaces of Protected Virtual Machines( 2021)Both AMD and Intel have presented technologies for confidential computing in cloud environments. The proposed solutions - AMD SEV (-ES, -SNP) and Intel TDX - protect Virtual Machines (VMs) against attacks from higher privileged layers through memory encryption and integrity protection. This model of computation draws a new trust boundary between virtual devices and the VM, which in so far lacks thorough examination. In this paper, we therefore present an analysis of the virtual device interface and discuss several attack vectors against a protected VM. Further, we develop and evaluate VIA, an automated analysis tool to detect cases of improper sanitization of input recieved via the virtual device interface. VIA improves upon existing approaches for the automated analysis of device interfaces in the following aspects: (i) support for virtualization relevant buses, (ii) efficient Direct Memory Access (DMA) support and (iii) performance. VIA builds upon the Linux Kernel Library and clang's libfuzzer to fuzz the communication between the driver and the device via MMIO, PIO, and DMA. An evaluation of VIA shows that it performs 570 executions per second on average and improves performance compared to existing approaches by an average factor of 2706. Using VIA, we analyzed 22 drivers in Linux 5.10.0-rc6, thereby uncovering 50 bugs and initiating multiple patches to the virtual device driver interface of Linux. To prove our findings criticality under the threat model of AMD SEV and Intel TDX, we showcase three exemplary attacks based on the bugs found. The attacks enable a malicious hypervisor to corrupt the memory and gain code execution in protected VMs with SEV-ES and are theoretically applicable to SEV-SNP and TDX.
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PublicationChosen Ciphertext k-Trace Attacks on Masked CCA2 Secure Kyber( 2021)
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PublicationCloud Property Graph: Connecting Cloud Security Assessments with Static Code Analysis( 2021)In this paper, we present the Cloud Property Graph (CloudPG), which bridges the gap between static code analysis and runtime security assessment of cloud services. The CloudPG is able to resolve data flows between cloud applications deployed on different resources, and contextualizes the graph with runtime information, such as encryption settings. To provide a vendorand technology-independent representation of a cloud service's security posture, the graph is based on an ontology of cloud resources, their functionalities and security features. We show, using an example, that our CloudPG framework can be used by security experts to identify weaknesses in their cloud deployments, spanning multiple vendors or technologies, such as AWS, Azure and Kubernetes. This includes misconfigurations, such as publicly accessible storages or undesired data flows within a cloud service, as restricted by regulations such as GDPR.
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PublicationLeveraging Edge Computing and Differential Privacy to Securely Enable Industrial Cloud Collaboration Along the Value Chain( 2021)
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PublicationActivation Anomaly Analysis( 2021)Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.
