The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data- generating models are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multi-arm trials with binary outcome. The only of the common data-generating models which is directly applicable to a random-effects network setting uses strongly restrictive assumptions. To overcome these limitations, we modify this approach and derive a related simulation procedure using odds ratios as effect measure. The performance of this procedure is evaluated with synthetic data and in an empirical example.

Quantum-AI Synthetic Data Generator Market Outlook

According to our latest research, the global Quantum-AI Synthetic Data Generator market size reached USD 1.82 billion in 2024, reflecting a robust expansion driven by technological advancements and increasing adoption across multiple industries. The market is projected to grow at a CAGR of 32.7% from 2025 to 2033, reaching a forecasted market size of USD 21.69 billion by 2033. This growth trajectory is primarily fueled by the rising demand for high-quality synthetic data to train artificial intelligence models, address data privacy concerns, and accelerate digital transformation initiatives across sectors such as healthcare, finance, and retail.

One of the most significant growth factors for the Quantum-AI Synthetic Data Generator market is the escalating need for vast, diverse, and privacy-compliant datasets to train advanced AI and machine learning models. As organizations increasingly recognize the limitations and risks associated with using real-world data, particularly regarding data privacy regulations like GDPR and CCPA, the adoption of synthetic data generation technologies has surged. Quantum computing, when integrated with artificial intelligence, enables the rapid and efficient creation of highly realistic synthetic datasets that closely mimic real-world data distributions while ensuring complete anonymity. This capability is proving invaluable for sectors like healthcare and finance, where data sensitivity is paramount and regulatory compliance is non-negotiable. As a result, organizations are investing heavily in Quantum-AI synthetic data solutions to enhance model accuracy, reduce bias, and streamline data sharing without compromising privacy.

Another key driver propelling the market is the growing complexity and volume of data generated by emerging technologies such as IoT, autonomous vehicles, and smart devices. Traditional data collection methods are often insufficient to keep pace with the data requirements of modern AI applications, leading to gaps in data availability and quality. Quantum-AI Synthetic Data Generators address these challenges by producing large-scale, high-fidelity synthetic datasets on demand, enabling organizations to simulate rare events, test edge cases, and improve model robustness. Additionally, the capability to generate structured, semi-structured, and unstructured data allows businesses to meet the specific needs of diverse applications, ranging from fraud detection in banking to predictive maintenance in manufacturing. This versatility is further accelerating market adoption, as enterprises seek to future-proof their AI initiatives and gain a competitive edge.

The integration of Quantum-AI Synthetic Data Generators into cloud-based platforms and enterprise IT ecosystems is also catalyzing market growth. Cloud deployment models offer scalability, flexibility, and cost-effectiveness, making synthetic data generation accessible to organizations of all sizes, including small and medium enterprises. Furthermore, the proliferation of AI-driven analytics in sectors such as retail, e-commerce, and telecommunications is creating new opportunities for synthetic data applications, from enhancing customer experience to optimizing supply chain operations. As vendors continue to innovate and expand their service offerings, the market is expected to witness sustained growth, with new entrants and established players alike vying for market share through strategic partnerships, product launches, and investments in R&D.

From a regional perspective, North America currently dominates the Quantum-AI Synthetic Data Generator market, accounting for over 38% of the global revenue in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology companies, robust investment in AI research, and favorable regulatory environment contribute to North America's leadership position. Europe is also witnessing significant growth, driven by stringent data privacy regulations and increasing adoption of AI across industries. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding IT infrastructure, and government initiatives promoting AI innovation. As regional markets continue to evolve, strategic collaborations and cross-border partnerships are expected to play a pivotal role in shaping the global landscape of the Quantum-AI Synthetic Data Generator market.

Component Analysis

The Synthea Generated Synthetic Data in FHIR hosts over 1 million synthetic patient records generated using Synthea in FHIR format. Exported from the Google Cloud Healthcare API FHIR Store into BigQuery using analytics schema . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . This public dataset is also available in Google Cloud Storage and available free to use. The URL for the GCS bucket is gs://gcp-public-data--synthea-fhir-data-1m-patients. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. Please cite SyntheaTM as: Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079

The data labeling market is experiencing robust growth, projected to reach $3.84 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 28.13% from 2025 to 2033. This expansion is fueled by the increasing demand for high-quality training data across various sectors, including healthcare, automotive, and finance, which heavily rely on machine learning and artificial intelligence (AI). The surge in AI adoption, particularly in areas like autonomous vehicles, medical image analysis, and fraud detection, necessitates vast quantities of accurately labeled data. The market is segmented by sourcing type (in-house vs. outsourced), data type (text, image, audio), labeling method (manual, automatic, semi-supervised), and end-user industry. Outsourcing is expected to dominate the sourcing segment due to cost-effectiveness and access to specialized expertise. Similarly, image data labeling is likely to hold a significant share, given the visual nature of many AI applications. The shift towards automation and semi-supervised techniques aims to improve efficiency and reduce labeling costs, though manual labeling will remain crucial for tasks requiring high accuracy and nuanced understanding. Geographical distribution shows strong potential across North America and Europe, with Asia-Pacific emerging as a key growth region driven by increasing technological advancements and digital transformation. Competition in the data labeling market is intense, with a mix of established players like Amazon Mechanical Turk and Appen, alongside emerging specialized companies. The market's future trajectory will likely be shaped by advancements in automation technologies, the development of more efficient labeling techniques, and the increasing need for specialized data labeling services catering to niche applications. Companies are focusing on improving the accuracy and speed of data labeling through innovations in AI-powered tools and techniques. Furthermore, the rise of synthetic data generation offers a promising avenue for supplementing real-world data, potentially addressing data scarcity challenges and reducing labeling costs in certain applications. This will, however, require careful attention to ensure that the synthetic data generated is representative of real-world data to maintain model accuracy. This comprehensive report provides an in-depth analysis of the global data labeling market, offering invaluable insights for businesses, investors, and researchers. The study period covers 2019-2033, with 2025 as the base and estimated year, and a forecast period of 2025-2033. We delve into market size, segmentation, growth drivers, challenges, and emerging trends, examining the impact of technological advancements and regulatory changes on this rapidly evolving sector. The market is projected to reach multi-billion dollar valuations by 2033, fueled by the increasing demand for high-quality data to train sophisticated machine learning models. Recent developments include: September 2024: The National Geospatial-Intelligence Agency (NGA) is poised to invest heavily in artificial intelligence, earmarking up to USD 700 million for data labeling services over the next five years. This initiative aims to enhance NGA's machine-learning capabilities, particularly in analyzing satellite imagery and other geospatial data. The agency has opted for a multi-vendor indefinite-delivery/indefinite-quantity (IDIQ) contract, emphasizing the importance of annotating raw data be it images or videos—to render it understandable for machine learning models. For instance, when dealing with satellite imagery, the focus could be on labeling distinct entities such as buildings, roads, or patches of vegetation.October 2023: Refuel.ai unveiled a new platform, Refuel Cloud, and a specialized large language model (LLM) for data labeling. Refuel Cloud harnesses advanced LLMs, including its proprietary model, to automate data cleaning, labeling, and enrichment at scale, catering to diverse industry use cases. Recognizing that clean data underpins modern AI and data-centric software, Refuel Cloud addresses the historical challenge of human labor bottlenecks in data production. With Refuel Cloud, enterprises can swiftly generate the expansive, precise datasets they require in mere minutes, a task that traditionally spanned weeks.. Key drivers for this market are: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Potential restraints include: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Notable trends are: Healthcare is Expected to Witness Remarkable Growth.

Synthetic data generated with stable diffution. Consists of 6,390 images. Real dataset used for generating: https://zenodo.org/records/10203721. stable diffusiton model (img2img) used for generating: https://github.com/AUTOMATIC1111/stable-diffusion-webui. denoising strength 0.1

Project (practical wotk for Bachelor's paper) where data is used for model training: https://github.com/rkalvitis/Bakalaurs.

To produce a domain-specific dataset, GPT-4 is assigned the role of an engineering design expert. Furthermore, the ontology, which signifies the design process and design entities, is integrated into the prompts to label the synthetic dataset and enhance the GPT model's grasp of the conceptual design process and domain-specific knowledge. Additionally, the CoT prompting technique compels the GPT models to clarify their reasoning process, thereby fostering a deeper understanding of the tasks.

Data Description

We release the synthetic data generated using the method described in the paper Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (ACL 2024 Findings). The external knowledge we use is based on external knowledge graphs.

  Generated Datasets

The original train/validation/test data, and the generated synthetic training data are listed as follows. For each dataset, we generate 5000 synthetic… See the full description on the dataset page: https://huggingface.co/datasets/ritaranx/clinical-synthetic-text-kg.

Dataset Card for finance-exam-data-generated

This dataset has been created with distilabel. The pipeline script was uploaded to easily reproduce the dataset: app.py. It can be run directly using the CLI: distilabel pipeline run --script "https://huggingface.co/datasets/1rsh/finance-exam-data-generated/raw/main/app.py"

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using… See the full description on the dataset page: https://huggingface.co/datasets/1rsh/finance-exam-data-generated.

FID value comparison between the real dataset of 1000 real images and the synthetic datasets of 1000 synthetic images generated from different GAN architectures which are modified to generate four channels outputs.

Round 3 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1008 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

This survey shows the plans of enterprises to make use of data generated by the internet of things (IoT), as of August 2017. Seventy percent of the respondents were reportedly already using that data to improve customer experience and a further ** percent were expecting to do so in the near future.

QUEST projects both used and produced an immense variety of global data sets that needed to be shared efficiently between the project teams. These global synthesis data sets are also a key part of QUEST's legacy, providing a powerful way of communicating the results of QUEST among and beyond the UK Earth System research community.

This dataset contains soil data generated from ISLSCP II.

The International Satellite Land Surface Climatology Project, Initiative II (ISLSCP II) is a follow on project from The International Satellite Land Surface Climatology Project (ISLSCP). ISLSCP II had the lead role in addressing land-atmosphere interactions - process modelling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.

This is the sequence data generated during the development of microsatellite markers for Cowslip. Additional resource: Charlotte Bickler, Stuart A’Hara, Joan Cottrell, Lucy Rogers & Jon Bridle 2013. Characterisation of thirteen polymorphic microsatellite markers for cowslip (Primula veris L.) developed using a 454 sequencing approach. Conservation Genet Resources 5:1135-1137.

La base des données contient les espèces types et endémiques d'Afrique Centrale pour les plantes vasculaire conservées à Lwiro (LWI). Ce projet couvre l’ensemble de trois pays d’Afrique Centrale entre autre, le Burundi, le Rwanda et la République Démocratique du Congo. La superficie géographique de cette zone est de 2399572 km2, avec respectivement 2345400 km2 pour la République Démocratique du Congo, 27834 km2 pour le Burundi et 26338 km2 pour le Rwanda.

Data Preparation Tools Market Outlook

The global data preparation tools market size was valued at USD 3.5 billion in 2023 and is projected to reach USD 12.8 billion by 2032, exhibiting a CAGR of 15.5% during the forecast period. The primary growth factors driving this market include the increasing adoption of big data analytics, the rising significance of data-driven decision-making, and growing technological advancements in AI and machine learning.

The surge in data-driven decision-making across various industries is a significant growth driver for the data preparation tools market. Organizations are increasingly leveraging advanced analytics to gain insights from massive datasets, necessitating efficient data preparation tools. These tools help in cleaning, transforming, and structuring raw data, thereby enhancing the quality of data analytics outcomes. As the volume of data generated continues to rise exponentially, the demand for robust data preparation tools is expected to grow correspondingly.

The integration of AI and machine learning technologies into data preparation tools is another crucial factor propelling market growth. These technologies enable automated data cleaning, error detection, and anomaly identification, thereby reducing manual intervention and increasing efficiency. Additionally, AI-driven data preparation tools can adapt to evolving data patterns, making them highly effective in dynamic business environments. This trend is expected to further accelerate the adoption of data preparation tools across various sectors.

As the demand for efficient data handling grows, the role of Data Infrastructure Construction becomes increasingly crucial. This involves building robust frameworks that support the seamless flow and management of data across various platforms. Effective data infrastructure construction ensures that data is easily accessible, securely stored, and efficiently processed, which is vital for organizations leveraging big data analytics. With the rise of IoT and cloud computing, constructing a scalable and flexible data infrastructure is essential for businesses aiming to harness the full potential of their data assets. This foundational work not only supports current data needs but also prepares organizations for future technological advancements and data growth.

The growing emphasis on regulatory compliance and data governance is also contributing to the market expansion. Organizations are required to adhere to strict regulatory standards such as GDPR, HIPAA, and CCPA, which mandate stringent data handling and processing protocols. Data preparation tools play a vital role in ensuring that data is compliant with these regulations, thereby minimizing the risk of data breaches and associated penalties. As regulatory frameworks continue to evolve, the demand for compliant data preparation tools is likely to increase.

Regionally, North America holds the largest market share due to the presence of major technology players and early adoption of advanced analytics solutions. Europe follows closely, driven by stringent data protection regulations and a strong focus on data governance. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid industrialization, increasing investments in big data technologies, and the growing adoption of IoT. Latin America and the Middle East & Africa are also anticipated to experience steady growth, supported by digital transformation initiatives and the expanding IT infrastructure.

Platform Analysis

The platform segment of the data preparation tools market is categorized into self-service data preparation, data integration, data quality, and data governance. Self-service data preparation tools are gaining significant traction as they empower business users to prepare data independently without relying on IT departments. These tools provide user-friendly interfaces and drag-and-drop functionalities, enabling users to quickly clean, transform, and visualize data. The rising need for agile and faster data preparation processes is driving the adoption of self-service platforms.

Data integration tools are essential for combining data from disparate sources into a unified view, facilitating comprehensive data analysis. These tools support the extraction, transformation, and loading (ETL) processes, ensuring data consistency and accuracy. With the increasing complexity of data environments and the need f

The data being generated and disseminated is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

The hyperscale data center industry is experiencing robust growth, projected to reach a market size of $101.23 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.29% from 2025 to 2033. This expansion is fueled by several key drivers. The exponential increase in data generated by cloud computing, the Internet of Things (IoT), and big data analytics necessitates massive data storage and processing capabilities, driving demand for hyperscale data centers. Furthermore, the increasing adoption of artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) applications further intensifies this demand. The shift towards digital transformation across various industries, coupled with the growing need for enhanced network connectivity and low latency, is also contributing significantly to market growth. Hyperscale colocation facilities are gaining traction, offering businesses a scalable and cost-effective alternative to self-build data centers. Competition among major players, including IBM, Hewlett Packard Enterprise, Alphabet, Cisco, Microsoft, Amazon Web Services, Huawei, Quanta Computer, Alibaba, Facebook, and Nvidia, is fierce, driving innovation and efficiency improvements within the sector. Geographical distribution reveals a strong presence in North America and Europe, driven by mature digital economies and robust IT infrastructure. However, the Asia-Pacific region is witnessing rapid growth, particularly in countries like India and China, fueled by increasing digitalization and government initiatives to support the development of digital infrastructure. Despite the positive growth trajectory, challenges remain. These include the high initial capital investment required for building and maintaining hyperscale data centers, the escalating energy consumption, and the growing concerns regarding data security and privacy. Addressing these challenges will be crucial for sustainable and responsible growth in the hyperscale data center market throughout the forecast period. The industry is likely to see further consolidation and strategic partnerships as companies seek to leverage economies of scale and expand their market reach. Recent developments include: November 2022 - Big Data Exchange (BDx), PT Indosat Tbk (Indosat Ooredoo Hutchison), and PT Aplikanusa Lintasarta announced their plan to build a 100MW data center complex on 12 acres of land. This new data center campus, CGK5, will be located in Karawang, West Java, east of Jakarta, and will be part of the company's third availability zone. The BDx Indonesia joint venture is a key component of the BDx platform, and the construction of CGK5 is BDx's 11th data center in the Asia-Pacific region. With more than USD 1 billion in committed investment funding, BDx's strong development trajectory across Asia allows scaled innovation in the most challenging markets., June 2022 - Equinix Inc., one of the leading global digital infrastructure companies, and PGIM Real Estate, the real estate investment and financing arm of PGIM, Prudential financial's global asset management business, announced the opening of the xScale data center in Sydney, named SY9x. This achievement followed the completion of the parties' USD 575 million joint venture., May 2022 - NTT Ltd in India announced the launch of its new hyperscale data center facility in Navi Mumbai, beginning with the NAV1A data center. This increases NTT's data center presence in the nation to 12 facilities, covering more than 2.5 million sq ft (232,258 m2) and 220 MW of facility power, solidifying its position as India's market leader in this segment., March 2022 - Yondr Group, one of the global leaders in developer, owner-operator, and service provider of data centers announced its expansion into the Malaysian market with a planned 200MW hyperscale campus to be developed on 72.8 acres of land acquired from TPM Technopark Sdn Bhd, a wholly owned subsidiary of Johor Corporation. Yondr's hyper-scale campus will be built in phases and have a total capacity of 200MW when completed, with the first phase anticipated to be completed in 2024. With at least 600MW of capacity, black fiber connectivity, and scalable utilities and infrastructure.. Key drivers for this market are: Growing Demand for Cloud Computing and Other High Performance Technologies. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Growing Demand for Cloud Computing and Other Hight Performance Technologies Driving the Market.

This dataset was generated during the precision testing of three water-quality sondes before picking one to use for field deployment of high frequency ground-water quality monitoring. Precision is important because the authors wanted to try and minimize calibration drift corrections between site visits. A laboratory experiment was conducted for the three sondes to simultaneously measure at hourly intervals with a setup of standard solution circulating past the sondes to simulate field conditions. The electrical conductivity experiment lasted 33 hours, the pH experiment lasted 13 hours, and the DO experiment failed (no data).

Introduction

This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.

NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.

Datasets

The firts dataset was colleted to train the detection models (D1) and other collected using different attacks than those used in training to test the models and ensure their generalization (D2).

The datasets contain both benign and malicious traffic. All collected datasets are balanced.

The version of NetFlow used to build the datasets is 5.

    Dataset
    Aim
    Samples
    Benign-malicious
    traffic ratio




    D1
    Training
    400,003
    50%


    D2
    Test
    57,239
    50%

Infrastructure and implementation

Two sets of flow data were collected with DOROTHEA. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor ipt_netflow installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.

DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)

Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).

The malicious traffic collected (SQLI attacks) was performed using SQLMAP. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.

The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.

    Parameters
    Description




    '--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'
    Enumerate users, password hashes, privileges, roles, databases, tables and columns


    --level=5
    Increase the probability of a false positive identification


    --risk=3
    Increase the probability of extracting data


    --random-agent
    Select the User-Agent randomly


    --batch
    Never ask for user input, use the default behavior


    --answers="follow=Y"
    Predefined answers to yes

Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the MYSQL or SQLServer database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).

The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24. The malicious traffic in the test sets was collected under different conditions. For D1, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.

However, for D2, BlindSQL SQLIAs were performed against the web form connected to a PostgreSQL database. The IP address spaces of the networks were also different from those of D1. In D2, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.

To run the MySQL server we ran MariaDB version 10.4.12. Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.