Abstract Security must be considered in almost every software system. Unfortunately, selecting and implementing security features remains a challenge due to the wide variety of security threats and possible countermeasures. While security standards are intended to help developers, they are usually too abstract and vague to help implementing security features, or they merely help configuring such. A resource that describes security features at an abstraction level that lies between high-level (i.e., rather too general) and low-level (i.e., rather too specific) security standards could facilitate secure systems development. This resource should support the selection of appropriate security features to achieve high-level security goals, allow easy retrieval of relevant low-level details, and provide pointers to suitable ways to realize the security features. To realize security features, developers typically use external security libraries or frameworks, to minimize implementation mistakes. Even when using libraries, developers still make mistakes when writing code to integrate them, often resulting in security vulnerabilities. When security incidents occur or the system needs to be audited or maintained, it is essential to know what security features have been implemented and, more importantly, where they are located. This task, commonly referred to as feature location, is often tedious and error-prone. While dedicated feature location techniques exist, they require significant manual effort or adherence to strict development processes, preventing their use. Therefore, we have to support long-term tracking of implemented security features. We present a study of security features presented in the literature and their coverage in popular security frameworks. We contribute (1) a taxonomy of 68 functional implementation-level security features including a mapping to widely used security standards, (2) an examination of 21 popular security frameworks concerning which of these security features they provide, and (3) a discussion on the representation of security features in source code. Our taxonomy aims to aid developers in selecting appropriate security features and security frameworks, as well as relating them to security standards when they need to choose and implement security features for a software system.

Abstract Architecture recovery tools help software engineers obtain an overview of the structure of their software systems during all phases of the software development life cycle. This is especially important for microservice applications because they consist of multiple interacting microservices, which makes it more challenging to oversee the architecture. Various tools and techniques for architecture recovery (also called architecture reconstruction) have been presented in academic and gray literature sources, but no overview and comparison of their accuracy exists. This paper presents the results of a multivocal literature review with the goal of identifying architecture recovery tools for microservice applications and a comparison of the identified tools’ architectural recovery accuracy. We focused on static tools since they can be integrated into fast-paced CI/CD pipelines. 13 such tools were identified from the literature and nine of them could be executed and compared on their capability of detecting different system characteristics. The best-performing tool exhibited an overall F1-score of 0.86. Additionally, the possibility of combining multiple tools to increase the recovery correctness was investigated, yielding a combination of four individual tools that achieves an F1-score of 0.91.

Infrastructure as Code (IaC) automates IT infrastructure deployment, which is particularly beneficial for continuous releases, for instance, in the context of microservices and cloud systems. Despite its flexibility in application architecture, neglecting security can lead to vulnerabilities. The lack of comprehensive architectural security guidelines for IaC poses challenges in adhering to best practices. We studied how developers interpret IaC scripts (source code) in two IaC technologies, Ansible and Terraform, compared to semi-formal IaC deployment architecture models and metrics regarding design-level security understanding. In a controlled experiment involving ninety-four participants, we assessed the understandability of IaC-based deployment architectures through source code inspection compared to semi-formal representations in models and metrics. We hypothesized that providing semi-formal IaC deployment architecture models and metrics as supplementary material would significantly improve the comprehension of IaC security-related practices, as measured by task correctness . Our findings suggest that semi-formal IaC deployment architecture models and metrics as supplementary material enhance the understandability of IaC security-related practices without significantly increasing duration . We also observed a significant correlation between task correctness and duration when models and metrics were provided.

Models 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.

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.

Dataflow 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.

The 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.

Demand Response (DR) is getting the focus of the energy efficiency directives in European energy commission. It counts as an important instrument for improving efficiency of the energy system from final consumption to generation, transmission and distribution system. Integration of the DR into the electricity market is one of the measures which can take part in ensuring the reliability and security of the energy system in near future. In this regard various methods and tools developed to analyze the effect of DR in the different domains which range across voltage regulation, regulating reserve, market efficiency and transmission congestion management. This paper will not introduce a new method but will analyze the potential of DR in Austrian residential sector using GridLAB-D. A case-study generator is developed to generate any desired test cases and the results were analyzed in order to assess the residential demand response potential.

In developing system interconnects, it is important to be able to analyze the performance as early in the design process as possible. Since modern interconnect behaviors are often too complex to be captured by analytical modeling, simulation is generally seen as the best solution to get performance measurement results. Early in the design process, the system is modeled at a high level of abstraction and a complete system model is not available. Therefore, a flexible framework for workload generation is needed. We believe that classical workload generation techniques from the network domain are not sufficient to capture the behavior of closely coupled modules. We propose a reactive workload generation scheme that is suitable for simulation of communication patterns. In our framework, workloads are generated by specifying use cases that are based on events and resulting actions. The post-processing analysis then provides insights into model performance for a specified generated workload.