Sandbox Environment Research Articles

Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence.

Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence.

Technology Selection of High-Voltage Offshore Substations Based on Artificial Intelligence

This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency.

A summit on a Global Patient co-Owned Cloud (GPOC)

BackgroundDuring the last thirty years the world has benefited from the increasing adoption to cloud-based storage of personal health records. The concept of a Global Patient co-Owned Cloud (GPOC) of personal health records is presented in the GPOC Series, which contains a systematic review and meta-analysis, a global survey among 100% of the UN member states and a technical sandbox environment. GPOC contains patient co-ownership of personal health records. The global survey showed a consensus for the realisation of a GPOC.MethodsHere, we present a Delphi styled GPOC Summit. It also contains a final vote, that validates the results of the previous GPOC Survey. Hence, the Summit contains both multiple rounds of discussion and a voting.ResultsAt the GPOC Summit a unanimous consensus for patient co-ownership of the personal health records was expressed. The majority of participants underlined that GPOC may benefit global health, transfers of the personal health records globally and the dissemination of evidence-based medicine across the globe. The plausible advantages for patients, both refugees and travellers and for developing economies were underlined. GPOC, as a large substrate for global medical artificial intelligence research, was highlighted. There was unanimous positive interest for GPOC. The GPOC Summit agreed on patient co-ownership of personal health records. This collective agreement underscores the potential of GPOC to enhance global health outcomes, facilitate seamless exchange of health data, and advance medical research worldwide.ConclusionsThe summit emphasised the potential benefits of GPOC for diverse populations and economies, highlighting its potential role in improving healthcare access and infrastructure. Moving forward, coordination with relevant initiatives could enhance interoperability and standardisation in healthcare data management. In conclusion, the GPOC Summit represents one step towards realising a patient-centric, globally accessible healthcare ecosystem, with possible implications for the development and global adoption of AI-driven healthcare solutions.

Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center.

The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of oncology research, our team at Vanderbilt University Medical Center (VUMC) enabled our Epic-based EHR to be compatible with the Minimal Common Oncology Data Elements (mCODE), which is a Fast Healthcare Interoperability Resources (FHIR)-based consensus data standard created to facilitate the transmission of EHRs for patients with cancer. Our approach used an extract, transform, load tool for converting EHR data from the VUMC Epic Clarity database into mCODE-compatible profiles. We established a sandbox environment on Microsoft Azure for data migration, deployed a FHIR server to handle application programming interface (API) requests, and mapped VUMC data to align with mCODE structures. In addition, we constructed a web application to demonstrate the practical use of mCODE profiles in health care. We developed an end-to-end pipeline that converted EHR data into mCODE-compliant profiles, as well as a web application that visualizes genomic data and provides cancer risk assessments. Despite the complexities of aligning traditional EHR databases with mCODE standards and the limitations of FHIR APIs in supporting advanced statistical methodologies, this project successfully demonstrates the practical integration of mCODE standards into existing health care infrastructures. This study provides a proof of concept for the interoperability of mCODE within a major health care institution's EHR system, highlighting both the potential and the current limitations of FHIR APIs in supporting complex data analysis for oncology research.

GrowSpace: A reinforcement learning environment for plant architecture

Plants adapt over time to their surrounding conditions. We argue that no current plant simulator addresses plant responses to an environmental stimulus over time through gamification and sequential decision making. We propose the GrowSpace simulator that is built within a reinforcement learning framework, providing a sandbox environment to test out hypotheses. GrowSpace currently focuses on the behavioral response of plants to light displacement, with the objective of controlling a plant’s shape by moving a light source. The back-end of the simulator is implemented using the Space Colonisation Algorithm. This simulator serves as a low-dimensional test bed to visualize plant growth and movement with respect to a light stimulus and to better understand a plant’s reactions to these conditions. GrowSpace provides an artificial modular environment that is simulated to the advancements of plant biological systems all while using a reinforcement learning framework. Tasks are crafted and tested with artificial intelligence algorithms to further understand the concept of plant growth control through light. We equally demonstrate that sequential control of plant branching is hard challenge for the plant science community as well as the reinforcement learning community.

Implementing a sandbox approach in health technology assessment: benefits and recommendations.

The sandbox approach, developed in the financial technologies sector, creates an environment to collaboratively develop and test innovative new products, methods and regulatory approaches, separated from business as usual. It has been used in health care to encourage innovation in response to emerging challenges, but, until recently, has not been used in health technology assessment (HTA). This article summarizes our learnings from using the sandbox approach to address three challenges facing HTA organizations and to identify implications for the use of this approach in HTA. We identified three challenging contemporary HTA-related topics to explore in a sandbox environment, away from the pressures and interests of "live" assessments. We convened a pool of 120 stakeholders and experts to participate in various sandbox activities and ultimately co-develop solutions to help HTA organizations respond to the identified challenges. Important general learnings about the potential benefits and implementation of a sandbox approach in HTA were identified. Consequently, we developed recommendations to guide its use, including how to implement an HTA sandbox in an effective way and the types of challenges for which it may be best suited. For many HTA organizations, it is difficult to carefully consider emerging challenges and innovate their processes due to risks associated with decision errors and resource limitations. The sandbox approach could reduce these barriers. The potential benefits of addressing HTA challenges in a collaborative "safe space" are considerable.

Installation of Regulatory Sandbox Environment from the Perspective of the Brazilian Charter for Smart Cities

The development of smart and sustainable cities is becoming a real necessity for Brazilian municipalities. As such, the objective of this article was to present the concept of smart cities, as well as the guidelines proposed by the Brazilian Charter for Smart Cities. Subsequently, the Regulatory Sandbox in cities is presented. It is an instrument proposed in the Charter and regulated by Federal Complementary Law No. 182/2021, and can also be installed in Brazilian municipalities, through temporary Government authorization. Its institution aims to develop innovative business models and test new technologies, with a view not only to improving the quality of life of its population but also as an instrument to be used to actively participate in the construction of a smart city. In this way, the analysis of the scientific and academic production carried out about the Regulatory Sandbox in Brazil was decided upon. The methodology used was analytical research, through literature review. The results showed that digital transformation is a fundamental change in cities and, the institution of a Sandbox model is a secure way to test and validate resource-saving technologies.

The TikTok of Teaching: The Pedagogical Possibilities of Collaborative Digital Ethnography

Working collaboratively with students, lecturers, and others, to conduct digital ethnography enriches the ethnographies produced, expands pedagogical possibilities, and allows us to rethink how we teach and do research in anthropology. This Special Issue is an output of such a collaborative attempt. In this Editorial we introduce the TikTok Ethnography Collective and the collaborative mode of research and learning we established in September 2020. The articles we have collated demonstrate that collaborative ethnographic methods are the ideal tool for researching algorithmically shaped digital spaces. But more than this, by sharing our collective experience, we make the case for incorporating collaborative methods into anthropological teaching and learning in order to disrupt traditional, hierarchical models of education and research. We propose that inclusion of students in the research process is imperative for facilitating a safe, creative sandbox environment that allows staff and students to explore and formulate theories and reflections somewhat liberated from the expectations around who should and should not be the expert. We invite readers to join us in considering the broader implications of embracing collaborative research and teaching methods.

Designing an Ethical and Secure Pain Estimation System Using AI Sandbox for Contactless Healthcare

Pain estimation in patients having communication difficulties is vital for preventing adverse consequences such as misdiagnosis, delayed treatment, and increased suffering. Traditional pain assessment tools relying on observer-based ratings and patient self-reporting are hampered by subjectivity and the need for continuous human monitoring, which have the potential to lead to inaccurate or delayed pain estimation. This paper presents an extensive literature review, a conceptual framework, and a systematic procedure for helping researchers develop a contactless, multimodal pain estimation system that leverages AI-based automation of standard pain assessment tools and scales within an AI sandbox environment. Our proposed concept aims to improve the efficiency of traditional pain estimation systems while reducing subjectivity and physical contact. This approach offers potential benefits, such as more accurate and timely pain assessment, reduced burden on healthcare professionals, and improved patient experiences. Moreover, the integration of the AI sandbox allows researchers and developers to experiment with AI models, algorithms, and systems safely and securely, ensuring that AI systems are reliable and robust before deployment. We also discuss potential challenges and ethical considerations related to the use of AI in pain estimation, emphasizing the importance of addressing these concerns to ensure the safe and responsible integration of this technology into healthcare systems. The paper lays a foundation for future research and innovation in pain management, ultimately contributing to better patient care and advancements in the field.

Optimization on compressive strength of sandwich plates with internal arborescent microstructure

The subject of this work is the optimization of a topology for sandwich plates with an internal arborescent microstructure. Here, we propose a parametric approach to the optimization using visual programming methods in the Dynamo Sandbox environment. An internal structure of the sandwich plate was optimized in terms of minimizing the weight of the entire structure in relation to the compressive strength. Several variable parameters in the optimization process were applied: lengths of individual tree levels, their cross-sections and locations of nodes. To validate obtained results of the optimization process, a 3D model of the plate was printed and tested on compressive strength. The results were also analysed and compared with other authors’ research.

Infrastructure as Code for Cybersecurity Training

An organization's infrastructure rests upon the premise that cybersecurity professionals have specific knowledge in administrating and protecting it against outside threats. Without this expertise, sensitive information could be leaked to malicious actors and cause damage to critical systems. These attacks tend to become increasingly specialized, meaning cybersecurity professionals must ensure proficiency in specific areas. Naturally, recommendations include creating advanced practical training scenarios considering realistic situations to help trainees gain detailed knowledge. However, the caveats of high-cost infrastructure and difficulties in the deployment process of this kind of system, primarily due to the manual process of pre-configuring software needed for the training and relying on a set of static Virtual Machines, may take much work to circumvent. In order to facilitate this process, our work addresses the use of Infrastructure as Code (IaC) and DevOps to automate the deployment of cyber ranges. An approach closely related to virtualization and containerization as the code's underlying infrastructure helps lay down this burden. Notably, placing emphasis on using IaC tools like Ansible eases the process of configuration management and provisioning of a network. Therefore, we start by focusing on understanding what the State of the Art perspectives lack and showcasing the benefits of this new working outlook. Lastly, we explore several up-to-date vulnerabilities that are constantly messing with the lives of individuals and organizations, most related to Privilege Escalation, Remote Code Execution attacks, and Incident Forensics, allowing the improvement of skills concerning Red team and Blue team scenarios. The analysis of the attacks and exploitation of such vulnerabilities are carried out safely due to a sandbox environment. The expected results revolve around using IaC to deploy a set of purposely-designed cyber ranges with specific challenges. The main objective is to guarantee a complexity of scenarios similar to what we can observe in enterprise-level networks. Thus, this entails having a set of playbooks that can be run in a machine or laboratory, assuring the final state of the network is consistent. We expect this deployment strategy to be cost-effective, allowing the trainee to get deep insight into a wide range of situations. Nowadays, DevOps solutions work as a silver bullet against the issues derived from old-case-driven approaches for setting up scenarios. In short, one of the key takeaways of this work is contributing to better prepare specialists in ensuring that the principles of the National Institute of Standards and Technology (NIST) Cybersecurity Framework hold, namely: prevent, detect, mitigate, and recover.

A Simple Recipe for Cooking your AI-assisted Dish to Serve it in the International Digital Specimen Architecture

With the rise of Artificial Intelligence (AI), a large set of new tools and services is emerging that supports specimen data mapping, standards alignment, quality enhancement and enrichment of the data. These tools currently operate in isolation, targeted to individual collections, collection management systems and institutional datasets. To address this challenge, DiSSCo, the Distributed System of Scientific Collections, is developing a new infrastructure for digital specimens, transforming them into actionable information objects. This infrastructure incorporates a framework for annotation and curation that allows the objects to be enriched or enhanced by both experts and machines. This creates the unique possibility to plug-in AI-assisted services that can then leverage digital specimens through this infrastructure, which serves as a harmonised Findable, Accessible, Interoperable and Reusable (FAIR) abstraction layer on top of individual institutional systems or datasets. An early example of such services are the ones developed in the Specimen Data Refinery workflow (Hardisty et al. 2022). The new architecture, DS Arch or Digital Specimen Architecture, is built on the concept of FAIR Digital Objects (FDO) (Islam et al. 2020). All digital specimens and related objects are served with persistent identifiers and machine-readable FDO records with information for machines about the object together with a pointer to its machine-readable type description. The type describes the structure of the object, its attributes and describes allowed operations. The digital specimen type and specimen media type are based on existing Biodiversity Information Standards (TDWG) such as Darwin Core, Access to Biological Collection Data (ABCD) Schema and Audiovisual Core Multimedia Resources Metadata Schema, and include support for annotation operations based on the World Wide Web Consortium (W3C) Annotations Data Model. This enables AI-assisted services registered with DS Arch to autonomously discover digital specimen objects and determine the actions they are authorised to perform. AI-assisted services can facilitate various tasks such as digitisation, extract new information from specimen images, create relations with other objects or standardise data. These operations can be done autonomously, upon user request, or in tandem with expert validation. AI-assisted services registered with DS Arch, can interact in the same way with all digital specimens worldwide when served through DS Arch with their uniform FDO representation, even if the content richness, level of standardisation and scope of the specimen is different. DS Arch has been designed to serve digital specimens for living and preserved specimens, and preserved environmental, earth system and astrogeology samples. With the AI-assisted services, data can be annotated with new data, alternative values, corrections, and with new entity relationships. As a result, the digital specimens become Digital Extended Specimens enabling new science and application (Webster et al. 2021). With the implementation of a sophisticated trust model in DS Arch for community acceptance, these annotations will become part of the data itself and can be made available for inclusion in source systems such as collection management systems and aggregators such as Global Biodiversity Information Facility (GBIF), Geoscience Collections Access Service (GeoCASe) and Catalogue of Life. We aim to demonstrate in the session how AI-assisted services can be registered and used to annotate specimen data. Although the DiSSCo DS Arch is still in development and planned to become operational in 2025, we already have a sandbox environment available in which the concept can be tested and AI-assisted services can be piloted to act on digital specimen data. For testing purposes, the operations on specimens are currently limited to individual specimens and open data, however batch operations will also be possible in the future production environment.

Malware Analysis and Static Call Graph Generation with Radare2

"A powerful feature used in automated malware analysis is the static call graph of the executable file. Elimination of sandbox environment, fast scan, function call patterns beyond instruction level information – all of these motivate the prevalence of the feature. Processing and storing the static call graph of malicious samples in a scaled manner facilitates the application of complex network analysis in malware research. IDA Pro is one of the leading disassembler tools in the industry and can generate the call graph via GenCallGdl and GenFuncGdl APIs – a tool which was used in our previous works. In this paper an alternative analysis method is presented using another disassembler tool, Radare2, an open-source Unixbased software, which is also frequently used in this domain. Radare2 has Python support (among other languages), via the r2pipe package, thus enabling full scalability on Linux-based servers using containerized solutions. This paper offers a detailed technical description on how to use Radare2 to generate the static call graph of a PE file and a thorough comparison with the output of IDA Pro, as well as a public dataset on which the experiments were carried out. 2010 Mathematics Subject Classification. 68P25, 68P30. 1998 CR Categories and Descriptors. D.4.6 [Security and Protection]: Subtopic – Invasive software. Key words and phrases. malware analysis, static call graph, radare2, IDA Pro."

Recognition of tor malware and onion services

The transformation of the contemporary societies through digital technologies has had a profound effect on all human activities including those that are in the realm of illegal, unlawful, and criminal deeds. Moreover, the affordances provided by the anonymity creating techniques such as the Tor protocol which are beneficial for preserving civil liberties, appear to be highly profitable for various types of miscreants whose crimes range from human trafficking, arms trading, and child pornography to selling controlled substances and racketeering. The Tor similar technologies are the foundation of a vast, often mysterious, sometimes anecdotal, and occasionally dangerous space termed as the Dark Web. Using the features that make the Internet a uniquely generative knowledge agglomeration, with no borders, and permeating different jurisdictions, the Dark Web is a source of perpetual challenges for both national and international law enforcement agencies. The anonymity granted to the wrong people increases the complexity and the cost of identifying both the crimes and the criminals, which is often exacerbated with lack of proper human resources. Technologies such as machine learning and artificial intelligence come to the rescue through automation, intensive data harvesting, and analysis built into various types of web crawlers to explore and identify dark markets and the people behind them. It is essential for an effective and efficient crawling to have a pool of dark sites or onion URLs. The research study presents a way to build a crawling mechanism by extracting onion URLs from malicious executables by running them in a sandbox environment and then analysing the log file using machine learning algorithms. By discerning between the malware that uses the Tor network and the one that does not, we were able to classify the Tor using malware with an accuracy rate of 91% with a logistic regression algorithm. The initial results suggest that it is possible to use this machine learning approach to diagnose new malicious servers on the Tor network. Embedding this kind of mechanism into the crawler may also induce predictability, and thus efficiency in recognising dark market activities, and consequently, their closure.

Can Fintech Promote Sustainable Finance? Policy Lessons from the Case of Turkey

This study contributes to sustainable finance literature by exemplifying promotion of sustainable finance through fintech solutions for emerging market economies by presenting the case of Turkey. Turkey is one of the largest emerging market economies in the world with a strong banking system and high adoption of technology, so it has great potential to benefit from fintech solutions to boost sustainable finance. For the case analysis, the data used came from a research platform for a Turkish start-up ecosystem, Turkish regulations, and documents released on Turkey’s sustainable finance strategies by Turkish and international institutions. We found that Turkey has made remarkable progress in increasing financial inclusivity for underbanked individuals and SMEs via providing contactless payment and contract systems and microfinance by mobile carriers and other online platforms. Turkey was also able to promote the responsible consumption goal for sustainable development by improving fintech solutions on payment systems with educational content on this goal. With upcoming developments such as the sandbox environment in Istanbul Financial Center, fintech solutions using Big Data, AI, and blockchain could emerge much faster with collaboration between banking and fintech sectors and regulatory institutions to better assess climate-related financial risks and form a national carbon trading mechanism.

JAX-ReaxFF: A Gradient-Based Framework for Fast Optimization of Reactive Force Fields.

The reactive force field (ReaxFF) model bridges the gap between traditional classical models and quantum mechanical (QM) models by incorporating dynamic bonding and polarizability. To achieve realistic simulations using ReaxFF, model parameters must be optimized against high fidelity training data which typically come from QM calculations. Existing parameter optimization methods for ReaxFF consist of black box techniques using genetic algorithms or Monte Carlo methods. Due to the stochastic behavior of these methods, the optimization process oftentimes requires millions of error evaluations for complex parameter fitting tasks, thereby significantly hampering the rapid development of high quality parameter sets. Rapid optimization of the parameters is essential for developing and refining Reax force fields because producing a force field which exhibits empirical accuracy in terms of dynamics typically requires multiple refinements to the training data as well as to the parameters under optimization. In this work, we present JAX-ReaxFF, a novel software tool that leverages modern machine learning infrastructure to enable fast optimization of ReaxFF parameters. By calculating gradients of the loss function using the JAX library, JAX-ReaxFF utilizes highly effective local optimization methods that are initiated from multiple guesses in the high dimensional optimization space to obtain high quality results. Leveraging the architectural portability of the JAX framework, JAX-ReaxFF can execute efficiently on multicore CPUs, graphics processing units (GPUs), or even tensor processing units (TPUs). As a result of using the gradient information and modern hardware accelerators, we are able to decrease ReaxFF parameter optimization time from days to mere minutes. Furthermore, the JAX-ReaxFF framework can also serve as a sandbox environment for domain scientists to explore customizing the ReaxFF functional form for more accurate modeling.

The Evolution of Volatile Memory Forensics

The collection and analysis of volatile memory is a vibrant area of research in the cybersecurity community. The ever-evolving and growing threat landscape is trending towards fileless malware, which avoids traditional detection but can be found by examining a system’s random access memory (RAM). Additionally, volatile memory analysis offers great insight into other malicious vectors. It contains fragments of encrypted files’ contents, as well as lists of running processes, imported modules, and network connections, all of which are difficult or impossible to extract from the file system. For these compelling reasons, recent research efforts have focused on the collection of memory snapshots and methods to analyze them for the presence of malware. However, to the best of our knowledge, no current reviews or surveys exist that systematize the research on both memory acquisition and analysis. We fill that gap with this novel survey by exploring the state-of-the-art tools and techniques for volatile memory acquisition and analysis for malware identification. For memory acquisition methods, we explore the trade-offs many techniques make between snapshot quality, performance overhead, and security. For memory analysis, we examined the traditional forensic methods used, including signature-based methods, dynamic methods performed in a sandbox environment, as well as machine learning-based approaches. We summarize the currently available tools, and suggest areas for more research.

On the effectiveness of binary emulation in malware classification

Malware authors continuously evolve their code base to include counter-analysis methods that can significantly hinder their detection and blocking. While malware execution in a sandboxed environment may provide insightful feedback about what the malware does in a machine, anti-virtualisation and hooking evasion methods may allow malware to bypass such detection methods. The main objective of this work is to complement sandbox execution with the use of binary emulation frameworks. The core idea is to exploit the fact that binary emulation frameworks may test samples quicker than a sandbox environment as they do not need to open a whole new virtual machine to execute the binary. While with this approach we lose the granularity of the data collected through a sandbox, one may only need to efficiently determine whether a file is malicious or to which malware family it belongs. To this end, we record the performed API calls and use them to explore the efficacy of using them as features for binary and multiclass classification. Our extensive experiments with real-world malware illustrate that this approach is very accurate, achieving state-of-the art outcomes with a statistically robust set of classification experiments while simultaneously having a relatively low computational overhead compared to traditional sandbox approaches. In fact, we compare the binary analysis results with a commercial sandbox, and our classification outperforms it at the expense of the fine-grained results that a sandbox provides.

Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics.

The value of digital twins for prototyping controllers or interventions in a sandbox environment are well-established in engineering and physics. However, this is challenging for biophysics trying to seamlessly compose models of multiple spatial and temporal scale behavior into the digital twin. Two challenges stand out as constraining progress: (i) ensuring physical consistency of conservation laws across composite models and (ii) drawing useful and timely clinical and scientific information from conceptually and computationally complex models. Challenge (i) can be robustly addressed with bondgraphs. However, challenge (ii) is exacerbated using this approach. The complexity question can be looked at from multiple angles. First from the perspective of discretizations that reflect underlying biophysics (functional tissue units) and secondly by exploring maximum entropy as the principle guiding multicellular biophysics. Statistical mechanics, long applied to understanding emergent phenomena from atomic physics, coupled with the observation that cellular architecture in tissue is orchestrated by biophysical constraints on metabolism and communication, shows conceptual promise. This architecture along with cell specific properties can be used to define tissue specific network motifs associated with energetic contributions. Complexity can be addressed based on energy considerations and finding mean measures of dependent variables. A probability distribution of the tissue's network motif can be approximated with exponential random graph models. A prototype problem shows how these approaches could be implemented in practice and the type of information that could be extracted.

Coordinated use of Health Level 7 standards to support clinical decision support: Case study with shared decision making and drug-drug interactions

BackgroundDespite advances in interoperability standards, it remains challenging and often costly to share clinical decision support (CDS) across healthcare organizations. This is due in part to limited coordination among CDS components. To improve coordination of CDS components, Health Level 7 (HL7) has developed a suite of interoperability standards with Fast Health Interoperability Resources (FHIR) specification as a common information model. Evidence is needed to determine the feasibility of implementing these CDS components; therefore, the objective of this study was to investigate the coordination of emerging HL7 standards with modular CDS architecture components. MethodsWe used a modular, standards-based architecture consisting of four components: data, logic, services, and applications. The implementation use-case was an application to support shared decision making in the context of drug-drug interactions (DDInteract). ResultsDDInteract uses FHIR as the data representation model, Clinical Quality Language for logic representation, CDS Hooks for the services layer, and Substitutable Medical Apps Reusable Technologies for application integration. DDInteract was first implemented in a sandbox environment and then in an electronic health record (Epic®) test environment. DDInteract can be integrated in clinical workflows through on-demand access from a menu or through CDS Hooks upon opening a patient’s record or placing a medication order. ConclusionIn the context of drug interactions, DDInteract is the first application to leverage a full stack of emerging interoperability standards for each component of modular CDS architecture. The demonstrated feasibility of interoperable components can be generalized to other modular CDS applications.