Dataset Card for example-preference-dataset

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/example-preference-dataset/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/distilabel-internal-testing/example-generate-preference-dataset.

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.

This training data was generated using GPT-4o as part of the 'Drawing with LLM' competition (https://www.kaggle.com/competitions/drawing-with-llms). It can be used to fine-tune small language models for the competition or serve as an augmentation dataset alongside other data sources.

The dataset is generated in two steps using the GPT-4o model. - In the first step, topic descriptions relevant to the competition are generated using a specific prompt. By running this prompt multiple times, over 3,000 descriptions were collected.

 
prompt=f""" I am participating in an SVG code generation competition.
  
   The competition involves generating SVG images based on short textual descriptions of everyday objects and scenes, spanning a wide range of categories. The key guidelines are as follows:
  
   - Descriptions are generic and do not contain brand names, trademarks, or personal names.
   - No descriptions include people, even in generic terms.
   - Descriptions are concise—each is no more than 200 characters, with an average length of about 50 characters.
   - Categories cover various domains, with some overlap between public and private test sets.
  
   To train a small LLM model, I am preparing a synthetic dataset. Could you generate 100 unique topics aligned with the competition style?
  
   Requirements:
   - Each topic should range between **20 and 200 characters**, with an **average around 60 characters**.
   - Ensure **diversity and creativity** across topics.
   - **50% of the topics** should come from the categories of **landscapes**, **abstract art**, and **fashion**.
   - Avoid duplication or overly similar phrasing.
  
   Example topics:
                 a purple forest at dusk, gray wool coat with a faux fur collar, a lighthouse overlooking the ocean, burgundy corduroy, pants with patch pockets and silver buttons, orange corduroy overalls, a purple silk scarf with tassel trim, a green lagoon under a cloudy sky, crimson rectangles forming a chaotic grid,  purple pyramids spiraling around a bronze cone, magenta trapezoids layered on a translucent silver sheet,  a snowy plain, black and white checkered pants,  a starlit night over snow-covered peaks, khaki triangles and azure crescents,  a maroon dodecahedron interwoven with teal threads.
  
   Please return the 100 topics in csv format.
   """
 

• In the second step, SVG code is generated by prompting the GPT-4o model. The following prompt is used to query the model to generate svg.

 
  prompt = f"""
      Generate SVG code to visually represent the following text description, while respecting the given constraints.
      
      Allowed Elements: `svg`, `path`, `circle`, `rect`, `ellipse`, `line`, `polyline`, `polygon`, `g`, `linearGradient`, `radialGradient`, `stop`, `defs`
      Allowed Attributes: `viewBox`, `width`, `height`, `fill`, `stroke`, `stroke-width`, `d`, `cx`, `cy`, `r`, `x`, `y`, `rx`, `ry`, `x1`, `y1`, `x2`, `y2`, `points`, `transform`, `opacity`
      

      Please ensure that the generated SVG code is well-formed, valid, and strictly adheres to these constraints. 
      Focus on a clear and concise representation of the input description within the given limitations. 
      Always give the complete SVG code with nothing omitted. Never use an ellipsis.

      The code is scored based on similarity to the description, Visual question anwering and aesthetic components.
      Please generate a detailed svg code accordingly.

      input description: {text}
      """
 

The raw SVG output is then cleaned and sanitized using a competition-specific sanitization class. After that, the cleaned SVG is scored using the SigLIP model to evaluate text-to-SVG similarity. Only SVGs with a score above 0.5 are included in the dataset. On average, out of three SVG generations, only one meets the quality threshold after the cleaning, sanitization, and scoring process.

A dataset with ~50,000 samples for SVG code generation is publicly available at: https://huggingface.co/datasets/vinoku89/svg-code-generation

The item published is a dataset that provides the raw data and original code to generate Figure 4 in the research paper, Correlative non-destructive techniques to investigate ageing and orientation effects in automotive Li-ion pouch cells, https://doi.org/10.5522/04/c.6868027 of which I am first author. The measurements and following data analysis took place between January 2022 – November 2022.

The figure illustrates the ultrasonic mapping measurements of pouch cells that have been extracted from electric vehicles and have been aged in real-world conditions. The degradation of the cells was measured using four different complementary characterisation measurement techniques, one of which was ultrasonic mapping.

The ultrasonic mapping measurements were performed using an Olympus Focus PX phased-array instrument (Olympus Corp., Japan) with a 5 MHz 1D linear phased array probe consisting of 64 transducers. The transducer had an active aperture of 64 mm with an element pitch (centre-to-centre distance between elements) of 1 mm. The cell was covered with ultrasonic couplant (Fannin UK Ltd.), prior to every scan to ensure good acoustic transmission. The transducer was moved along the length of each cell at a fixed pressure using an Olympus GLIDER 2-axis encoded scanner with the step size set at 1 mm to give a resolution of ca. 1 mm2. Due to the large size of the cells, the active aperture of the probe was wide enough to cover 1/3 the width, meaning that three measurements for each cell were taken and the data was combined to form the colour maps.

Data from the ultrasonic signals were analysed using FocusPC software. The waveforms recorded by the transducer were exported and plotted using custom Python code to compare how the signal changes at different points in the cell. For consistency, a specific ToF range was selected for all cells, chosen because it is where the part of the waveform, known as the ‘echo-peak’, is located.74 The echo-peak is useful to monitor as it is where the waveform has travelled the whole way through the cell and reflected from the back surface, so characterising the entire cell. The maximum amplitude of the ultrasonic signal within this ToF range, at each point, are combined to produce a colour map. The signal amplitude is a percentage proportion of 100 where 100 is the maximum intensity of the signal, meaning that the signal has been attenuated the least as it travels through the cell, and 0 is the minimum intensity. The intensity is absolute and not normalised across all scans, meaning that an amplitude values on different cells can be directly compared. The Pristine cell is a second-generation Nissan Leaf pouch, different to the first-generation aged cells of varying orientation. The authors were not able to acquire an identical first-generation pristine Nissan Leaf cell. Nonetheless, it was expected that the Pristine cell would contain a uniform internal structure regardless of the specific chemistry and this would be identified in an ultrasound map consisting of a single colour (or narrow colour range).

Data sets used to prepare illustrative figures for the overview article “Multiscale Modeling of Background Ozone” Overview The CMAQ model output datasets used to create illustrative figures for this overview article were generated by scientists in EPA/ORD/CEMM and EPA/OAR/OAQPS. The EPA/ORD/CEMM-generated dataset consisted of hourly CMAQ output from two simulations. The first simulation was performed for July 1 – 31 over a 12 km modeling domain covering the Western U.S. The simulation was configured with the Integrated Source Apportionment Method (ISAM) to estimate the contributions from 9 source categories to modeled ozone. ISAM source contributions for July 17 – 31 averaged over all grid cells located in Colorado were used to generate the illustrative pie chart in the overview article. The second simulation was performed for October 1, 2013 – August 31, 2014 over a 108 km modeling domain covering the northern hemisphere. This simulation was also configured with ISAM to estimate the contributions from non-US anthropogenic sources, natural sources, stratospheric ozone, and other sources on ozone concentrations. Ozone ISAM results from this simulation were extracted along a boundary curtain of the 12 km modeling domain specified over the Western U.S. for the time period January 1, 2014 – July 31, 2014 and used to generate the illustrative time-height cross-sections in the overview article. The EPA/OAR/OAQPS-generated dataset consisted of hourly gridded CMAQ output for surface ozone concentrations for the year 2016. The CMAQ simulations were performed over the northern hemisphere at a horizontal resolution of 108 km. NO2 and O3 data for July 2016 was extracted from these simulations generate the vertically-integrated column densities shown in the illustrative comparison to satellite-derived column densities. CMAQ Model Data The data from the CMAQ model simulations used in this research effort are very large (several terabytes) and cannot be uploaded to ScienceHub due to size restrictions. The model simulations are stored on the /asm archival system accessible through the atmos high-performance computing (HPC) system. Due to data management policies, files on /asm are subject to expiry depending on the template of the project. Files not requested for extension after the expiry date are deleted permanently from the system. The format of the files used in this analysis and listed below is ioapi/netcdf. Documentation of this format, including definitions of the geographical projection attributes contained in the file headers, are available at https://www.cmascenter.org/ioapi/ Documentation on the CMAQ model, including a description of the output file format and output model species can be found in the CMAQ documentation on the CMAQ GitHub site at https://github.com/USEPA/CMAQ. This dataset is associated with the following publication: Hogrefe, C., B. Henderson, G. Tonnesen, R. Mathur, and R. Matichuk. Multiscale Modeling of Background Ozone: Research Needs to Inform and Improve Air Quality Management. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-6, (2020).

The data set was used to produce tables and figures in paper. This dataset is associated with the following publications: Lytle, D., S. Pfaller, C. Muhlen, I. Struewing, S. Triantafyllidou, C. White, S. Hayes, D. King, and J. Lu. A Comprehensive Evaluation of Monochloramine Disinfection on Water Quality, Legionella and Other Important Microorganisms in a Hospital. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 189: 116656, (2021). Lytle, D., C. Formal, K. Cahalan, C. Muhlen, and S. Triantafyllidou. The Impact of Sampling Approach and Daily Water Usage on Lead Levels Measured at the Tap. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 197: 117071, (2021).

Now based on the above two tables (UserTable and VisitorLogData), you need to create an input feature set for the Marketing Model.

Input Feature table:

UserID

Unique ID of the registered user

No_of_days_Visited_7_Days

How many days a user was active on platform in the last 7 days.

No_Of_Products_Viewed_15_Days

Number of Products viewed by the user in the last 15 days

User_Vintage

Vintage (In Days) of the user as of today

Most_Viewed_product_15_Days

Most frequently viewed (page loads) product by the user in the last 15 days. If there are multiple products that have a similar number of page loads then , consider the recent one. If a user has not viewed any product in the last 15 days then put it as Product101.

Most_Active_OS

Most Frequently used OS by user.

Recently_Viewed_Product

Most recently viewed (page loads) product by the user. If a user has not viewed any product then put it as Product101.

Pageloads_last_7_days

Count of Page loads in the last 7 days by the user

Clicks_last_7_days

Count of Clicks in the last 7 days by the user

Process to create Input Feature:

In the current case, you are supposed to generate an input feature set as on 28-May-2018. So, the visitor table is from 07-May-2018 to 27-May-2018.

As a Data Engineer Creating ETL Pipeline would definitely be appreciated and provide you the added advantage in interviews, Your effort should be to build ETL Pipeline such that passing the information of user data and log data, It can generate the input feature table automatically

The dataset are the data used to produce figure in manuscript. This dataset is associated with the following publication: Tang, M., D. Lytle, and J. Botkins. Accumulation and Release of Arsenic from Cast Iron: Impact of Initial Arsenic and Orthophosphate Concentrations. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 194: 116942, (2021).

Key is the core element of constructing Bitcoin trust network. The key usually consists of private key and public key. The private key is used to generate signatures and the public key is used to generate addresses. Bitcoin keys are generated by the elliptic curve algorithm SECP256k1. This data set contains the core code to generate the key.

sql-create-context-v2 Dataset

  Overview

The sql-create-context-v2 dataset enhances the original dataset built from WikiSQL and Spider, focusing on text-to-SQL tasks with a special emphasis on reducing hallucination of column and table names. This version introduces a JSONL format for more efficient data processing and iteration, alongside a structured approach to representing SQL queries in the dataset entries.

  Key Enhancements

Dataset Format: Transitioned to… See the full description on the dataset page: https://huggingface.co/datasets/ramachetan22/sql-create-context-v2.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

Performance evaluation based on AUC for pairs with counts > 1.

Pollinator habitat can be planted on farms to enhance floral and nesting resources, and subsequently, pollinator populations. There is ample evidence linking such plantings to greater pollinator abundance on farms, but less is known about their effects on pollinator reproduction. We placed Bombus impatiens Cresson (Hymenoptera: Apidae) and Megachile rotundata (F.) (Hymenoptera: Megachilidae) nests out on 19 Mid-Atlantic farms in 2018, where half (n=10) the farms had established wildflower plantings and half (n=9) did not. Bombus impatiens nests were placed at each farm in spring and mid-summer and repeatedly weighed to capture colony growth. We quantified the relative production of reproductive castes and assessed parasitism rates by screening for conopid fly parasitism and Nosema spores within female workers. We also released M. rotundata cocoons at each farm in spring and collected new nests and emergent adult offspring over the next year, recording female weight as an indicator of reproductive potential and quantifying Nosema parasitism and parasitoid infection rates. Bombus impatiens nests gained less weight and contained female workers with Nosema spore loads over 150x greater on farms with wildflower plantings. In contrast, M. rotundata female offspring weighed more on farms with wildflower plantings and marginally less on farms with honey bee hives. We conclude that wildflower plantings likely enhance reproduction in some species, but that they could also enhance microsporidian parasitism rates in susceptible bee species. It will be important to determine how wildflower planting benefits can be harnessed while minimizing parasitism in wild and managed bee species.

This archive contains code and data for reproducing the analysis for “Replication Data for Revisiting ‘The Rise and Decline’ in a Population of Peer Production Projects”. Depending on what you hope to do with the data you probabbly do not want to download all of the files. Depending on your computation resources you may not be able to run all stages of the analysis. The code for all stages of the analysis, including typesetting the manuscript and running the analysis, is in code.tar. If you only want to run the final analysis or to play with datasets used in the analysis of the paper, you want intermediate_data.7z or the uncompressed tab and csv files. The data files are created in a four-stage process. The first stage uses the program “wikiq” to parse mediawiki xml dumps and create tsv files that have edit data for each wiki. The second stage generates all.edits.RDS file which combines these tsvs into a dataset of edits from all the wikis. This file is expensive to generate and at 1.5GB is pretty big. The third stage builds smaller intermediate files that contain the analytical variables from these tsv files. The fourth stage uses the intermediate files to generate smaller RDS files that contain the results. Finally, knitr and latex typeset the manuscript. A stage will only run if the outputs from the previous stages do not exist. So if the intermediate files exist they will not be regenerated. Only the final analysis will run. The exception is that stage 4, fitting models and generating plots, always runs. If you only want to replicate from the second stage onward, you want wikiq_tsvs.7z. If you want to replicate everything, you want wikia_mediawiki_xml_dumps.7z.001 wikia_mediawiki_xml_dumps.7z.002, and wikia_mediawiki_xml_dumps.7z.003. These instructions work backwards from building the manuscript using knitr, loading the datasets, running the analysis, to building the intermediate datasets. Building the manuscript using knitr This requires working latex, latexmk, and knitr installations. Depending on your operating system you might install these packages in different ways. On Debian Linux you can run apt install r-cran-knitr latexmk texlive-latex-extra. Alternatively, you can upload the necessary files to a project on Overleaf.com. Download code.tar. This has everything you need to typeset the manuscript. Unpack the tar archive. On a unix system this can be done by running tar xf code.tar. Navigate to code/paper_source. Install R dependencies. In R. run install.packages(c("data.table","scales","ggplot2","lubridate","texreg")) On a unix system you should be able to run make to build the manuscript generalizable_wiki.pdf. Otherwise you should try uploading all of the files (including the tables, figure, and knitr folders) to a new project on Overleaf.com. Loading intermediate datasets The intermediate datasets are found in the intermediate_data.7z archive. They can be extracted on a unix system using the command 7z x intermediate_data.7z. The files are 95MB uncompressed. These are RDS (R data set) files and can be loaded in R using the readRDS. For example newcomer.ds <- readRDS("newcomers.RDS"). If you wish to work with these datasets using a tool other than R, you might prefer to work with the .tab files. Running the analysis Fitting the models may not work on machines with less than 32GB of RAM. If you have trouble, you may find the functions in lib-01-sample-datasets.R useful to create stratified samples of data for fitting models. See line 89 of 02_model_newcomer_survival.R for an example. Download code.tar and intermediate_data.7z to your working folder and extract both archives. On a unix system this can be done with the command tar xf code.tar && 7z x intermediate_data.7z. Install R dependencies. install.packages(c("data.table","ggplot2","urltools","texreg","optimx","lme4","bootstrap","scales","effects","lubridate","devtools","roxygen2")). On a unix system you can simply run regen.all.sh to fit the models, build the plots and create the RDS files. Generating datasets Building the intermediate files The intermediate files are generated from all.edits.RDS. This process requires about 20GB of memory. Download all.edits.RDS, userroles_data.7z,selected.wikis.csv, and code.tar. Unpack code.tar and userroles_data.7z. On a unix system this can be done using tar xf code.tar && 7z x userroles_data.7z. Install R dependencies. In R run install.packages(c("data.table","ggplot2","urltools","texreg","optimx","lme4","bootstrap","scales","effects","lubridate","devtools","roxygen2")). Run 01_build_datasets.R. Building all.edits.RDS The intermediate RDS files used in the analysis are created from all.edits.RDS. To replicate building all.edits.RDS, you only need to run 01_build_datasets.R when the intermediate RDS files and all.edits.RDS files do not exist in the working directory. all.edits.RDS is generated from the tsv files generated by wikiq. This may take several hours. By default building the dataset will...

Excel spreadsheet of raw data used to generate Fig 2F.

The product data are six statistics that were estimated for the chemical concentration of lithium in the soil C horizon of the conterminous United States. The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 55 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lithium concentration.

Context

Companies and individuals are storing increasingly more data digitally; however, much of the data is unused because it is unclassified. How many times have you opened your downloads folder, found a file you downloaded a year ago and you have no idea what the contents are? You can read through those files individually but imagine doing that for thousands of files. All that raw data in storage facilities create data lakes. As the amount of data grows and the complexity rises, data lakes become data swamps. The potentially valuable and interesting datasets will likely remain unused. Our tool addresses the need to classify these large pools of data in a visually effective and succinct manner by identifying keywords in datasets, and classifying datasets into a consistent taxonomy.

The files listed within kaggleDatasetSummaryTopicsClassification.csv have been processed with our tool to generate the keywords and taxonomic classification as seen below. The summaries are not generated from our system. Instead they were retrieved from user input as they uploaded the files on Kaggle. We planned to utilize these summaries to create an NLG model to generate summaries from any input file. Unfortunately we were not able to collect enough data to build a good model. Hopefully the data within this set might help future users achieve that goal.

Acknowledgements

Developed with Senior Design Center at NC State in collaboration with SAS. Senior Design Team: Tanya Chu, Katherine Marsh, Nikhil Milind, Anna Owens SAS Representatives: : Nancy Rausch, Marty Warner, Brant Kay, Tyler Wendell, JP Trawinski

Polygon shapefile showing the footprint boundaries, source agency origins, and resolutions of compiled bathymetric digital elevation models (DEMs) used to construct a continuous, high-resolution DEM of the southern portion of San Francisco Bay.

Synthetic Data Generation for Security Testing Market Outlook

According to our latest research, the global synthetic data generation for security testing market size reached USD 1.03 billion in 2024, reflecting robust growth driven by the rising need for enhanced cybersecurity and data privacy across industries. The market is projected to grow at a CAGR of 27.6% during the forecast period, reaching an estimated USD 8.56 billion by 2033. This expansion is primarily fueled by the increasing sophistication of cyber threats, stringent regulatory requirements, and the growing adoption of artificial intelligence and machine learning in security operations.

One of the most significant growth factors for the synthetic data generation for security testing market is the escalating complexity and frequency of cyberattacks targeting organizations worldwide. As cybercriminals continuously evolve their tactics, traditional security testing methods often fall short in identifying advanced threats. Synthetic data generation enables organizations to simulate a diverse range of attack scenarios without exposing real sensitive information, thereby enhancing the effectiveness of penetration testing, vulnerability assessment, and compliance testing. The ability to generate high-quality, representative data sets that mimic real-world conditions is empowering security teams to proactively identify and remediate vulnerabilities, ensuring robust protection of digital assets. Furthermore, the adoption of synthetic data is reducing the risk of data breaches during testing, which is particularly critical for highly regulated sectors such as BFSI and healthcare.

Another key driver propelling the market is the increasing regulatory scrutiny and emphasis on data privacy. Regulations such as GDPR in Europe, CCPA in California, and similar frameworks across the globe mandate strict controls over the use and sharing of personally identifiable information (PII). Synthetic data generation offers a compliant alternative by creating non-identifiable, artificial data sets for testing purposes, thereby minimizing the risk of non-compliance and hefty penalties. Organizations are leveraging synthetic data to ensure that their security testing processes adhere to legal requirements while still maintaining the integrity and realism needed for effective threat simulation. The convergence of data privacy and cybersecurity imperatives is expected to sustain high demand for synthetic data solutions in the coming years.

The rapid digital transformation across industries is also contributing to market growth. As enterprises accelerate their adoption of cloud computing, IoT, and AI-driven technologies, their attack surfaces are expanding, necessitating more rigorous and scalable security testing practices. Synthetic data generation tools are increasingly being integrated into DevSecOps pipelines, enabling continuous security validation throughout the software development lifecycle. This integration not only enhances security posture but also supports agile development by eliminating dependencies on real production data. The scalability, flexibility, and automation capabilities offered by modern synthetic data generation platforms are making them indispensable for organizations seeking to stay ahead of emerging cyber threats.

From a regional perspective, North America currently dominates the synthetic data generation for security testing market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major technology companies, a mature cybersecurity ecosystem, and early adoption of advanced security testing methodologies. Europe follows closely, driven by stringent data protection regulations and a strong focus on privacy-preserving technologies. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, increasing cyber threats, and growing regulatory awareness. Latin America and the Middle East & Africa are also emerging as promising markets, with organizations in these regions ramping up investments in cybersecurity infrastructure to address evolving threat landscapes.

Component Analysis

The component segment of the synthetic data generation for security testing market is bifurcated into software and services. Software solutions represent the core of this market, providing organizations with the tools necessary to generate, manipulate, and manage synthetic data sets tailored f

Overview

This dataset builds from sql-create-context. @misc{b-mc2_2023_sql-create-context, title = {sql-create-context Dataset}, author = {b-mc2}, year = {2023}, url = {https://huggingface.co/datasets/b-mc2/sql-create-context}, note = {This dataset was created by modifying data from the following sources: \cite{zhongSeq2SQL2017, yu2018spider}.}, }