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Item type:Publication, Access status: without files , Automatic rule checking for microservices: supporting security analysis with explainability(Association for Computing Machinery (ACM), 2026-07-16); ; ; ; Software security analysis is often done manually, raising performance and correctness issues. Introducing automation is challenging because human verification of the outcomes is often required, especially for security assessment and certification. The distributed nature of microservice applications further increases these concerns. We present an approach for automatically checking architectural security rules on models of microservice applications. It provides explainability for verdicts of rules that are expressed as model queries in our rule specification language. This comprehensible, step-by-step evidence leverages traceability information from the input models to link to artifacts in code. Hence, the complete analysis process from source code via model to rule verdict can be traced and verified. Custom rules can be formulated in addition to a library of 25 best-practice architectural security rules. We evaluated the approach’s correctness by checking the 25 rules on 16 dataflow diagrams of microservice applications with a prototype (called MicroCertiSec) and observed promising results (precision=0.98; recall=1). Additionally, we performed an evaluation with industry experts and academics to gain initial insights into the approach’s usefulness for real-world security analysis. The nine participants gave highly positive feedback on usefulness and usability and stated they would use such an approach in their daily work.Publicationtype: Journal Article36 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Detection strategies for microservice security tactics(2024-05-01); ; ; ; Microservice architectures are widely used today to implement distributed systems. Securing microservice architectures is challenging because of their polyglot nature, continuous evolution, and various security concerns relevant to such architectures. This article proposes a novel, model-based approach providing detection strategies to address the automated detection of security tactics (or patterns and best practices) in a given microservice architecture decomposition model. Our novel detection strategies are metrics-based rules that decide conformance to a security recommendation based on a statistical predictor. The proposed approach models this recommendation using Architectural Design Decisions (ADDs). We apply our approach for four different security-related ADDs on access management, traffic control, and avoiding plaintext sensitive data in the context of microservice systems. We then apply our approach to a model data set of 10 open-source microservice systems and 20 variants of those systems. Our results are detection strategies showing a very low bias, a very high correlation, and a low prediction error in our model data set.Publicationtype: Journal Article53 60 - 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 , Microservice Security Metrics for Secure Communication, Identity Management, and Observability(2023-02-13); ; ; ; Microservice architectures are increasingly being used to develop application systems. Despite many guidelines and best practices being published, architecting microservice systems for security is challenging. Reasons are the size and complexity of microservice systems, their polyglot nature, and the demand for the continuous evolution of these systems. In this context, to manually validate that security architecture tactics are employed as intended throughout the system is a time-consuming and error-prone task. In this article, we present an approach to avoid such manual validation before each continuous evolution step in a microservice system, which we demonstrate using three widely used categories of security tactics: secure communication, identity management, and observability. Our approach is based on a review of existing security guidelines, the gray literature, and the scientific literature, from which we derived Architectural Design Decisions (ADDs) with the found security tactics as decision options. In our approach, we propose novel detectors to detect these decision options automatically and formally defined metrics to measure the conformance of a system to the different options of the ADDs. We apply the approach to a case study data set of 10 open source microservice systems, plus another 20 variants of these systems, for which we manually inspected the source code for security tactics. We demonstrate and assess the validity and appropriateness of our metrics by performing an assessment of their conformance to the ADDs in our systems' dataset through statistical methods.Publicationtype: Journal Article38