P323 - Sicherheit 2022 - Sicherheit, Schutz und Zuverlässigkeit

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    Sicherheit medizintechnischer Protokolle im Krankenhaus
    (GI SICHERHEIT 2022, 2022) Saatjohann, Christoph; Ising, Fabian; Gierlings, Matthias; Noss, Dominik; Schimmler, Sascha; Klemm, Alexander; Grundmann, Leif; Frosch, Tilman; Schinzel, Sebastian; Christian Wressnegger, Delphine Reinhardt
    Medizinische Einrichtungen waren in den letzten Jahren immer wieder von Cyber-Angriffen betroffen. Auch wenn sich diese Angriffe derzeit auf die Office-IT-Infrastruktur der Einrichtungen konzentrieren, existiert mit medizinischen Systemen und Kommunikationsprotokollen eine weitere wenig beachtete Angriffsoberfläche. In diesem Beitrag analysieren wir die weit verbreiteten medizintechnischen Kommunikations-Protokolle DICOM und HL7 sowie Protokoll-Implementierungen auf ihre IT-Sicherheit. Dafür präsentieren wir die Ergebnisse der Sicherheitsanalyse der DICOM-und HL7-Standards, einen Fuzzer “MedFUZZ” für diese Protokolle sowie einen Schwachstellenscanner “MedVAS”, der Schwachstellen in medizintechnischen Produktivumgebungen auffinden kann.
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    On CRDTs in Byzantine Environments
    (GI SICHERHEIT 2022, 2022) Jacob, Florian; Bayreuther, Saskia; Hartenstein, Hannes; Christian Wressnegger, Delphine Reinhardt
    Conflict-free Replicated Data Types (CRDTs) allow updates to be applied to different replicas independently and concurrently, without the need for a remote conflict resolution. Thus, they provide a building block for scalability and performance of fault-tolerant distributed systems. Currently, CRDTs are typically used in a crash fault setting for global scale, partition-tolerant, highly available databases or collaborative applications. In this paper, we explore the use of CRDTs in Byzantine environments. This exploration is inspired by the popular Matrix messaging system: as recently shown, the underlying Matrix Event Graph replicated data type represents a CRDT that can very well deal with Byzantine behavior. This “Byzantine Tolerance” is due to mechanisms inherent in CRDTs and in the hash-based directed acyclic graph (HashDAG) data structure used in Matrix. These mechanisms restrict Byzantine behavior. We, therefore, discuss Byzantine behavior in a context of CRDTs, and how the notion of Byzantine tolerance relates to equivocation. We show that a subclass of CRDTs is equivocation-tolerant, i.e., without equivocation detection, prevention or remediation, this subclass still fulfills the CRDT properties, which leads to Byzantine tolerance. We conjecture that an operation-based Byzantine-tolerant CRDT design supporting non-commutative operations needs to be based on a HashDAG data structure. We close the paper with thoughts on chances and limits of this data type.
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    Recovering information from pixelized credentials
    (GI SICHERHEIT 2022, 2022) Garske, Viktor; Noack, Andreas; Christian Wressnegger, Delphine Reinhardt
    Pixelation is a common technique to redact sensitive information like credentials in images. In this paper, we propose a system that is able to recover information from pixelized text. Our contribution consists of a neural network as well as a generic pipeline that generates a realistic training dataset considering flexible specifications including wordlists, fonts, font sizes and letter spacings. The contributed neural network is a composition of a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN) using Long short-term memory (LSTM) and a Connectionist Temporal Classification (CTC) layer to decode sequences of characters. With our approach, we achieve a Label Error Rate (LER) under 50% when taking pixelation block sizes of up to 8 × 8 pixels on a 22pt font into account. Thereby, our results indicate that pixelation of sensitive data does not satisfy common privacy standards.
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    PrivacyDates: A Framework for More Privacy-Preserving Timestamp Data Types
    (GI SICHERHEIT 2022, 2022) Burkert, Christian; Balack, Jonathan; Federrath, Hannes; Christian Wressnegger, Delphine Reinhardt
    Case studies of application software data models indicate that timestamps are excessively used in connection with user activity. This contradicts the principle of data minimisation which demands a limitation to data necessary for a given purpose. Prior work has also identified common purposes of timestamps that can be realised by more privacy-preserving alternatives like counters and dates with purpose-oriented precision. In this paper, we follow up by demonstrating the real-world applicability of those alternatives. We design and implement three timestamp alternatives for the popular web development framework Django and evaluate their practicality by replacing conventional timestamps in the project management application Taiga.
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    Fighting Evasive Malware: How to Pass the Reverse Turing Test By Utilizing a VMI-Based Human Interaction Simulator
    (GI SICHERHEIT 2022, 2022) Gruber, Jan; Freiling, Felix C.; Christian Wressnegger, Delphine Reinhardt
    Sandboxes are an indispensable tool in dynamic malware analysis today. However, modern malware often employs sandbox-detection methods to exhibit non-malicious behavior within sandboxes and therefore evade automatic analysis. One category of sandbox-detection techniques are reverse Turing tests (RTTs) to determine the presence of a human operator. In order to pass these RTTs, we propose a novel approach which builds upon virtual machine introspection (VMI) to automatically reconstruct the graphical user interface, determine clickable buttons and inject human interface device events via direct control of virtualized human interface devices in a stealthy way. We extend the VMI-based open-source sandbox DRAKVUF with our approach and show that it successfully passes RTTs commonly employed by malware in the wild to detect sandboxes
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    Hardening the Security of Server-Aided MPC Using Remotely Unhackable Hardware Modules
    (GI SICHERHEIT 2022, 2022) Doerner, Dominik; Mechler, Jeremias; Müller-Quade, Jörn; Christian Wressnegger, Delphine Reinhardt
    Garbling schemes are useful building blocks for enabling secure multi-party computation (MPC), but require considerable computational resources both for the garbler and the evaluator. Thus, they cannot be easily used in a resource-restricted setting, e.g. on mobile devices. To circumvent this problem, server-aided MPC can be used, where circuit garbling and evaluation are performed by one or more servers. However, such a setting introduces additional points of failure: The servers, being accessible over the network, are susceptible to remote hacks. By hacking the servers, an adversary may learn all secrets, even if the parties participating in the MPC are honest. In this work, we investigate how the susceptibility for such remote hacks in the server-aided setting can be reduced. To this end, we modularize the servers performing the computationally intensive tasks. By using data diodes, air-gap switches and other simple remotely unhackable hardware modules, we can isolate individual components during large parts of the protocol execution, making remote hacks impossible at these times. Interestingly, this reduction of the attack surface comes without a loss of efficiency.
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    SICHERHEIT 2022 - Procreedings komplett
    (GI SICHERHEIT 2022, 2022) Fachbereich Sicherheit der Gesellschaft für Informatik e.V. (GI); Christian Wressnegger, Delphine Reinhardt
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    SMT-Based Verification of Concurrent Critical System
    (GI SICHERHEIT 2022, 2022) Güdemann, Matthias; Christian Wressnegger, Delphine Reinhardt
    Petri nets are a widely used formalism to describe and analyze critical systems. It is in particular well suited for systems with concurrency like cache coherence protocols, fault-tolerant distributed systems or security critical protocols. The verification approaches for Petri nets are most often based on enumerative approaches which allow for analyzing complex, often temporal, properties. Dataflow languages are widely used in safety critical systems. There are several state-of-the-art model-checkers for these languages. While the properties that can be verified are generally limited to invariants, it is possible to encode some interesting properties of Petri nets as invariants which makes them accessible for powerful analysis methods based on modern SMT and SAT solvers. The SpiNat approach transforms Petri net into synchronous dataflow language models. This allows for using predicate abstraction and the theory of unbounded integers allows to analyze the potentially unbounded markings of Petri nets using model-checking tools for languages like Lustre. The presented approach is orthogonal to enumeration based approaches for Petri net analysis and allows benefiting from any increase in efficiency of industrial strength SMT-based model-checkers like kind2 and Jkind
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    Differential Testing: How to find differences between programs that mostly behave identically?
    (GI SICHERHEIT 2022, 2022) Möller, Jonas; Christian Wressnegger, Delphine Reinhardt
    Differences between programs based on the same specification might lead to vulnerabilities that can not be detected by conventional testing. Differential testing is able to find these discrepancies by executing multiple programs on the same input and comparing their output. In this work, we discuss the fundamentals of differential testing and outline a general scheme for differential testing methods which is used to categorize and analyze current research approaches. Based on this, we formulate our own research questions which focus on how machine learning can aid differential testing
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    Towards Detection of Malicious Software Packages Through Code Reuse by Malevolent Actors
    (GI SICHERHEIT 2022, 2022) Ohm, Marc; Kempf, Lukas; Boes, Felix; Meier, Michael; Christian Wressnegger, Delphine Reinhardt
    Trojanized software packages used in software supply chain attacks constitute an emerging threat. Unfortunately, there is still a lack of scalable approaches that allow automated and timely detection of malicious software packages and thus most detections are based on manual labor and expertise. However, it has been observed that most attack campaigns comprise multiple packages that share the same or similar malicious code. We leverage that fact to automatically reproduce manually identified clusters of known malicious packages that have been used in real world attacks, thus, reducing the need for expert knowledge and manual inspection. Our approach, AST Clustering using MCL to mimic Expertise (ACME), yields promising results with a F1 score of 0.99. Signatures are automatically generated based on characteristic code fragments from clusters and are subsequently used to scan the whole npm registry for unreported malicious packages. We are able to identify and report six malicious packages that have been removed from npm consequentially. Therefore, our approach can support the detection by reducing manual labor and hence may be employed by maintainers of package repositories to detect possible software supply chain attacks through trojanized software packages.