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Item type:Publication, Access status: without files , In specs we trust? Conformance-analysis of implementation to specifications in Node-RED and associated security risksLow-code development frameworks for IoT platforms offer a simple drag-and-drop mechanism to create applications for the billions of existing IoT devices without the need for extensive programming knowledge. The security of such software is crucial given the close integration of IoT devices in many highly sensitive areas such as healthcare or home automation. Node-RED is a framework to build applications from nodes that are contributed by open-source developers. Its reliance on unvetted contributions and lack of security checks raises the concern that the applications could be vulnerable to attacks, thereby posing a security risk to end users. The low-code approach could imply that users lack the technical knowledge to mitigate or even realize such security concerns. This paper focuses on hidden information flows in Node-RED nodes, meaning flows that are not captured by the specifications. They could (unknowingly or with malicious intent) cause leaks of sensitive information to unauthorized entities. We report the results of a conformance analysis of all nodes in the Node-RED framework, for which we compared the numbers of specified inputs and outputs of each node against the number of sources and sinks detected with CodeQL. The results show, that 55% of all nodes exhibit more possible flows than are specified. A risk assessment of a subset of the nodes showed, that 28% of them are associated with a high severity and 36% with a medium severity rating.Publicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Designing secure AI-based systems: a multi-vocal literature review(IEEE, 2024-10); ; ; AI-based systems leverage recent advances in the field of AI/ML by combining traditional software systems with AI components. Applications are increasingly being developed in this way. Software engineers can usually rely on a plethora of supporting information on how to use and implement any given technology. For AI-based systems, however, such information is scarce. Specifically, guidance on how to securely design the architecture is not available to the extent as for other systems.We present 16 architectural security guidelines for the design of AI-based systems that were curated via a multi-vocal literature review. The guidelines could support practitioners with actionable advice on the secure development of AI-based systems. Further, we mapped the guidelines to typical components of AI-based systems and observed a high coverage where 6 out of 8 generic components have at least one guideline associated to them.Publicationtype: Conference Paper16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , How dataflow diagrams impact software security analysis : an empirical experimentModels of software systems are used throughout the software development lifecycle. Dataflow diagrams (DFDs), in particular, are well-established resources for security analysis. Many techniques, such as threat modelling, are based on DFDs of the analysed application. However, their impact on the performance of analysts in a security analysis setting has not been explored before. In this paper, we present the findings of an empirical experiment conducted to investigate this effect. Following a within-groups design, participants were asked to solve security-relevant tasks for a given microservice application. In the control condition, the participants had to examine the source code manually. In the model-supported condition, they were additionally provided a DFD of the analysed application and traceability information linking model items to artefacts in source code. We found that the participants (n = 24) performed significantly better in answering the analysis tasks correctly in the model-supported condition (41 % increase in analysis correctness). Further, participants who reported using the provided traceability information performed better in giving evidence for their answers (315% increase in correctness of evidence). Finally, we identified three open challenges of using DFDs for security analysis based on the insights gained in the experiment.Publicationtype: Conference Paper18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , microSecEnD: A dataset of security-enriched dataflow diagrams for microservice applicationsDataflow diagrams (DFDs) are useful resources in securing applications since they show a software system's architecture and allow assessing architectural security and weaknesses. Enriching them with annotations about implemented security features further strengthens this ability. This is especially true for microservice applications, as their most pressing security concerns stem from their separation into multiple services. Researchers need data to work on these issues and enhance microservices' architectural security. In this work, we present microSecEnD, a dataset of 17 manually created DFDs that are extensively annotated with information on implemented security features. We provide traceability for all model items. Further, a mapping to a list of 17 architectural security best-practices is provided. Finally, for each best-practice that an application violates, we present a model variant that does adhere to it.Publicationtype: Conference Paper54 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , GitHub Considered Harmful? Analyzing Open-Source Projects for the Automatic Generation of Cryptographic API Call SequencesGitHub is a popular data repository for code examples. It is being continuously used to train several AI-based tools to automatically generate code. However, the effectiveness of such tools in correctly demonstrating the usage of cryptographic APIs has not been thoroughly assessed. In this paper, we investigate the extent and severity of misuses, specifically caused by incorrect cryptographic API call sequences in GitHub. We also analyze the suitability of GitHub data to train a learning-based model to generate correct cryptographic API call sequences. For this, we manually extracted and analyzed the call sequences from GitHub. Using this data, we augmented an existing learning-based model called DeepAPI to create two security-specific models that generate cryptographic API call sequences for a given natural language (NL) description. Our results indicate that it is imperative to not neglect the misuses in API call sequences while using data sources like GitHub, to train models that generate code.Publicationtype: Conference Paper32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , STRIPED: A Threat Analysis Method for IoT SystemsCurrently, IoT systems display a poor level of security, as 50% of IoT devices are vulnerable to severe attacks, according to research. In an attempt to ameliorate the situation, we propose STRIPED, a threat analysis technique that focuses particularly on threat scenarios involving IoT devices that can be physically accessed by attackers. We evaluate STRIPED in a two-pronged way. First, we assess its performance compared to STRIDE (from which STRIPED is derived) in the context of a case study from the manufacturing industry. Second, we gather the feedback of 8 security experts working in a large, multinational company that specializes in secure IoT products for the domains of automotive, industrial, mobile and smart-home applications. These initial evaluation attempts provide encouraging evidence and suggest our method is a step in the right direction of facilitating security-by-design in IoT systems, especially industrial ones.Publicationtype: Conference Paper125 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards a Security Benchmark for the Architectural Design of Microservice ApplicationsThe microservice architecture presents many challenges from a security perspective, due to the large amount of services, leading to an increased attack surface and an unmanageble cognitive load for security analysts. Several benchmarks exist to guide the secure configuration of the deployment infrastructure for microservice applications, including containers (e.g., Docker), orchestration systems (e.g., Kubernetes), cloud platforms (e.g., AWS), and even operating systems (e.g., Linux). In this paper we approach the creation of a benchmark for the design of the microservice applications themselves. To this aim, we inventorize a number of relevant security rules for the architectural design of microservice applications and assess (in a preliminary way) how these rules could be checked automatically.Publicationtype: Conference Paper51 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , SoK: Security of Microservice Applications: A Practitioners' Perspective on Challenges and Best PracticesCloud-based application deployment is becoming increasingly popular among businesses, thanks to the emergence of microservices. However, securing such architectures is a challenging task since traditional security concepts cannot be directly applied to microservice architectures due to their distributed nature. The situation is exacerbated by the scattered nature of guidelines and best practices advocated by practitioners and organizations in this field. In this research paper we aim to shay light over the current microservice security discussions hidden within Grey Literature (GL) sources. Particularly, we identify the challenges that arise when securing microservice architectures, as well as solutions recommended by practitioners to address these issues. For this, we conducted a systematic GL study on the challenges and best practices of microservice security present in the Internet with the goal of capturing relevant discussions in blogs, white papers, and standards. We collected 312 GL sources from which 57 were rigorously classified and analyzed. This analysis on the one hand validated past academic literature studies in the area of microservice security, but it also identified improvements to existing methodologies pointing towards future research directions.Publicationtype: Conference Paper41 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Asset-driven Security Assurance Cases with Built-in Quality AssuranceSecurity Assurance Cases (SAC) are structured arguments and evidence bodies used to reason about security of a certain system. SACs are gaining focus in the automotive domain as the needs for security assurance are growing. In this study, we present an approach for creating SAC. The approach is inspired by the upcoming security standards ISO/SAE-21434 as well as the internal needs of automotive Original Equipment Manufacturers (OEMs). We created the approach by extracting relevant requirements from ISO/SAE-21434 and illustrated it using an example case of the head lamp items provided in the standard. We found that the approach is applicable and helps to satisfy the requirements for security assurance in the standard as well as the internal compliance needs in an automotive OEM.Publicationtype: Conference Paper134 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Remind: A framework for the resilient design of automotive systemsIn the past years, great effort has been spent on enhancing the security and safety of vehicular systems. Current advances in information and communication technology have increased the complexity of these systems and lead to extended functionalities towards self-driving and more connectivity. Unfortunately, these advances open the door for diverse and newly emerging attacks that hamper the security and, thus, the safety of vehicular systems. In this paper, we contribute to supporting the design of resilient automotive systems. We review and analyze scientific literature on resilience techniques, fault tolerance, and dependability. As a result, we present the REMIND resilience framework providing techniques for attack detection, mitigation, recovery, and resilience endurance. Moreover, we provide guidelines on how the REMIND framework can be used against common security threats and attacks and further discuss the trade-offs when applying these guidelines.Publicationtype: Conference Paper34