100+ datasets found
  1. d

    Website Analytics

    • catalog.data.gov
    • data.brla.gov
    • +2more
    Updated Jun 29, 2025
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    data.brla.gov (2025). Website Analytics [Dataset]. https://catalog.data.gov/dataset/website-analytics-89ba5
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    Dataset updated
    Jun 29, 2025
    Dataset provided by
    data.brla.gov
    Description

    Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

  2. d

    Web Traffic Data | 500M+ US Web Traffic Data Resolution | B2B and B2C...

    • datarade.ai
    .csv, .xls
    Updated Feb 22, 2024
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    Allforce (2024). Web Traffic Data | 500M+ US Web Traffic Data Resolution | B2B and B2C Website Visitor Identity Resolution [Dataset]. https://datarade.ai/data-products/traffic-continuum-from-solution-publishing-500m-us-web-traf-solution-publishing
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    .csv, .xlsAvailable download formats
    Dataset updated
    Feb 22, 2024
    Dataset authored and provided by
    Allforce
    Area covered
    United States
    Description

    Unlock the Potential of Your Web Traffic with Advanced Data Resolution

    In the digital age, understanding and leveraging web traffic data is crucial for businesses aiming to thrive online. Our pioneering solution transforms anonymous website visits into valuable B2B and B2C contact data, offering unprecedented insights into your digital audience. By integrating our unique tag into your website, you unlock the capability to convert 25-50% of your anonymous traffic into actionable contact rows, directly deposited into an S3 bucket for your convenience. This process, known as "Web Traffic Data Resolution," is at the forefront of digital marketing and sales strategies, providing a competitive edge in understanding and engaging with your online visitors.

    Comprehensive Web Traffic Data Resolution Our product stands out by offering a robust solution for "Web Traffic Data Resolution," a process that demystifies the identities behind your website traffic. By deploying a simple tag on your site, our technology goes to work, analyzing visitor behavior and leveraging proprietary data matching techniques to reveal the individuals and businesses behind the clicks. This innovative approach not only enhances your data collection but does so with respect for privacy and compliance standards, ensuring that your business gains insights ethically and responsibly.

    Deep Dive into Web Traffic Data At the core of our solution is the sophisticated analysis of "Web Traffic Data." Our system meticulously collects and processes every interaction on your site, from page views to time spent on each section. This data, once anonymous and perhaps seen as abstract numbers, is transformed into a detailed ledger of potential leads and customer insights. By understanding who visits your site, their interests, and their contact information, your business is equipped to tailor marketing efforts, personalize customer experiences, and streamline sales processes like never before.

    Benefits of Our Web Traffic Data Resolution Service Enhanced Lead Generation: By converting anonymous visitors into identifiable contact data, our service significantly expands your pool of potential leads. This direct enhancement of your lead generation efforts can dramatically increase conversion rates and ROI on marketing campaigns.

    Targeted Marketing Campaigns: Armed with detailed B2B and B2C contact data, your marketing team can create highly targeted and personalized campaigns. This precision in marketing not only improves engagement rates but also ensures that your messaging resonates with the intended audience.

    Improved Customer Insights: Gaining a deeper understanding of your web traffic enables your business to refine customer personas and tailor offerings to meet market demands. These insights are invaluable for product development, customer service improvement, and strategic planning.

    Competitive Advantage: In a digital landscape where understanding your audience can make or break your business, our Web Traffic Data Resolution service provides a significant competitive edge. By accessing detailed contact data that others in your industry may overlook, you position your business as a leader in customer engagement and data-driven strategies.

    Seamless Integration and Accessibility: Our solution is designed for ease of use, requiring only the placement of a tag on your website to start gathering data. The contact rows generated are easily accessible in an S3 bucket, ensuring that you can integrate this data with your existing CRM systems and marketing tools without hassle.

    How It Works: A Closer Look at the Process Our Web Traffic Data Resolution process is streamlined and user-friendly, designed to integrate seamlessly with your existing website infrastructure:

    Tag Deployment: Implement our unique tag on your website with simple instructions. This tag is lightweight and does not impact your site's loading speed or user experience.

    Data Collection and Analysis: As visitors navigate your site, our system collects web traffic data in real-time, analyzing behavior patterns, engagement metrics, and more.

    Resolution and Transformation: Using advanced data matching algorithms, we resolve the collected web traffic data into identifiable B2B and B2C contact information.

    Data Delivery: The resolved contact data is then securely transferred to an S3 bucket, where it is organized and ready for your access. This process occurs daily, ensuring you have the most up-to-date information at your fingertips.

    Integration and Action: With the resolved data now in your possession, your business can take immediate action. From refining marketing strategies to enhancing customer experiences, the possibilities are endless.

    Security and Privacy: Our Commitment Understanding the sensitivity of web traffic data and contact information, our solution is built with security and privacy at its core. We adhere to strict data protection regulat...

  3. Network Traffic Analysis Market - Size & Report 2025 - 2030

    • mordorintelligence.com
    pdf,excel,csv,ppt
    Updated Jun 21, 2025
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    Mordor Intelligence (2025). Network Traffic Analysis Market - Size & Report 2025 - 2030 [Dataset]. https://www.mordorintelligence.com/industry-reports/network-traffic-analysis-market
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    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Jun 21, 2025
    Dataset authored and provided by
    Mordor Intelligence
    License

    https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy

    Time period covered
    2019 - 2030
    Area covered
    Global
    Description

    Network Traffic Analysis Market is Segmented by Deployment (On-Premise, Cloud-Based, and Hybrid), Component (Solutions and Services), Organization Size (Large Enterprises and Small and Medium Enterprises), End-User Industry (BFSI, IT and Telecom, and More), and Geography. The Market Sizes and Forecasts are Provided in Value (in USD Million) for all the Above Segments.

  4. W

    Website Visitor Tracking Software Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Apr 25, 2025
    + more versions
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    Archive Market Research (2025). Website Visitor Tracking Software Report [Dataset]. https://www.archivemarketresearch.com/reports/website-visitor-tracking-software-561091
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    ppt, doc, pdfAvailable download formats
    Dataset updated
    Apr 25, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Website Visitor Tracking Software market is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. This market, currently valued at approximately $5 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both SMEs and large enterprises. Furthermore, the increasing sophistication of analytics features within these platforms allows for deeper insights into website traffic, user engagement, and conversion rates. This empowers businesses to personalize user experiences, refine marketing campaigns, and ultimately drive revenue growth. The market segmentation reveals a significant share held by cloud-based solutions due to their accessibility and flexibility, while the large enterprise segment is a primary revenue driver due to its higher spending capacity. Competition is intense, with established players like Google Analytics and Adobe Analytics alongside a burgeoning number of specialized providers offering unique features and functionalities. The competitive landscape is dynamic, with established players facing challenges from nimble startups innovating in areas like AI-powered behavioral analytics and personalized recommendations. While data privacy concerns and the increasing complexity of tracking regulations represent potential restraints, the overall market outlook remains positive. The continued growth of e-commerce, digital marketing, and the broader digital economy will fuel further demand for sophisticated visitor tracking solutions. Geographic expansion, particularly in rapidly developing economies within Asia-Pacific and Latin America, will contribute significantly to market expansion over the forecast period. The integration of website visitor tracking with other marketing automation and CRM tools is another key trend, further solidifying its importance within a comprehensive digital strategy. The market's future hinges on the continuous innovation in data analytics capabilities, the development of user-friendly interfaces, and the ability to adapt to evolving privacy regulations.

  5. Network traffic datasets created by Single Flow Time Series Analysis

    • zenodo.org
    • explore.openaire.eu
    • +1more
    csv, pdf
    Updated Jul 11, 2024
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    Josef Koumar; Josef Koumar; Karel Hynek; Karel Hynek; Tomáš Čejka; Tomáš Čejka (2024). Network traffic datasets created by Single Flow Time Series Analysis [Dataset]. http://doi.org/10.5281/zenodo.8035724
    Explore at:
    csv, pdfAvailable download formats
    Dataset updated
    Jul 11, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Josef Koumar; Josef Koumar; Karel Hynek; Karel Hynek; Tomáš Čejka; Tomáš Čejka
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Network traffic datasets created by Single Flow Time Series Analysis

    Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:

    J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.

    This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf

    In the following table is a description of each dataset file:

    File nameDetection problemCitation of original raw dataset
    botnet_binary.csv Binary detection of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.
    botnet_multiclass.csv Multi-class classification of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.
    cryptomining_design.csvBinary detection of cryptomining; the design part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022
    cryptomining_evaluation.csv Binary detection of cryptomining; the evaluation part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022
    dns_malware.csv Binary detection of malware DNS Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021.
    doh_cic.csv Binary detection of DoH

    Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020

    doh_real_world.csv Binary detection of DoH Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022
    dos.csv Binary detection of DoS Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.
    edge_iiot_binary.csv Binary detection of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.
    edge_iiot_multiclass.csvMulti-class classification of IoT malwareMohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.
    https_brute_force.csvBinary detection of HTTPS Brute ForceJan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020
    ids_cic_binary.csvBinary detection of intrusion in IDSIman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.
    ids_cic_multiclass.csv Multi-class classification of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.
    ids_unsw_nb_15_binary.csv Binary detection of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.
    ids_unsw_nb_15_multiclass.csv Multi-class classification of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.
    iot_23.csv Binary detection of IoT malware Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23
    ton_iot_binary.csv Binary detection of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021
    ton_iot_multiclass.csv Multi-class classification of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021
    tor_binary.csv Binary detection of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.
    tor_multiclass.csv Multi-class classification of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.
    vpn_iscx_binary.csv Binary detection of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.
    vpn_iscx_multiclass.csv Multi-class classification of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.
    vpn_vnat_binary.csv Binary detection of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022
    vpn_vnat_multiclass.csvMulti-class classification of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022

  6. m

    Encrypted Traffic Feature Dataset for Machine Learning and Deep Learning...

    • data.mendeley.com
    Updated Dec 6, 2022
    + more versions
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    Zihao Wang (2022). Encrypted Traffic Feature Dataset for Machine Learning and Deep Learning based Encrypted Traffic Analysis [Dataset]. http://doi.org/10.17632/xw7r4tt54g.1
    Explore at:
    Dataset updated
    Dec 6, 2022
    Authors
    Zihao Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This traffic dataset contains a balance size of encrypted malicious and legitimate traffic for encrypted malicious traffic detection and analysis. The dataset is a secondary csv feature data that is composed of six public traffic datasets.

    Our dataset is curated based on two criteria: The first criterion is to combine widely considered public datasets which contain enough encrypted malicious or encrypted legitimate traffic in existing works, such as Malware Capture Facility Project datasets. The second criterion is to ensure the final dataset balance of encrypted malicious and legitimate network traffic.

    Based on the criteria, 6 public datasets are selected. After data pre-processing, details of each selected public dataset and the size of different encrypted traffic are shown in the “Dataset Statistic Analysis Document”. The document summarized the malicious and legitimate traffic size we selected from each selected public dataset, the traffic size of each malicious traffic type, and the total traffic size of the composed dataset. From the table, we are able to observe that encrypted malicious and legitimate traffic equally contributes to approximately 50% of the final composed dataset.

    The datasets now made available were prepared to aim at encrypted malicious traffic detection. Since the dataset is used for machine learning or deep learning model training, a sample of train and test sets are also provided. The train and test datasets are separated based on 1:4. Such datasets can be used for machine learning or deep learning model training and testing based on selected features or after processing further data pre-processing.

  7. Z

    Network Traffic Analysis: Data and Code

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jun 12, 2024
    + more versions
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    Honig, Joshua (2024). Network Traffic Analysis: Data and Code [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_11479410
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    Dataset updated
    Jun 12, 2024
    Dataset provided by
    Moran, Madeline
    Honig, Joshua
    Chan-Tin, Eric
    Ferrell, Nathan
    Homan, Sophia
    Soni, Shreena
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Code:

    Packet_Features_Generator.py & Features.py

    To run this code:

    pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

    -h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

    Purpose:

    Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

    Uses Features.py to calcualte the features.

    startMachineLearning.sh & machineLearning.py

    To run this code:

    bash startMachineLearning.sh

    This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

    Options (to be edited within this file):

    --evaluate-only to test 5 fold cross validation accuracy

    --test-scaling-normalization to test 6 different combinations of scalers and normalizers

    Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

    --grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

    Purpose:

    Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

    Data

    Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

    Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

    First number is a classification number to denote what website, query, or vr action is taking place.

    The remaining numbers in each line denote:

    The size of a packet,

    and the direction it is traveling.

    negative numbers denote incoming packets

    positive numbers denote outgoing packets

    Figure 4 Data

    This data uses specific lines from the Virtual Reality.txt file.

    The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

    The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

    The .xlsx and .csv file are identical

    Each file includes (from right to left):

    The origional packet data,

    each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

    and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

  8. W

    Website Traffic Analysis Tool Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Apr 25, 2025
    + more versions
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    Data Insights Market (2025). Website Traffic Analysis Tool Report [Dataset]. https://www.datainsightsmarket.com/reports/website-traffic-analysis-tool-1455386
    Explore at:
    doc, ppt, pdfAvailable download formats
    Dataset updated
    Apr 25, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global website traffic analysis tool market is experiencing robust growth, driven by the increasing reliance on digital marketing and the need for businesses of all sizes to understand their online audience. The market, estimated at $15 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions provides scalability and cost-effectiveness for businesses, particularly SMEs seeking affordable analytics. Moreover, the evolution of sophisticated analytics features, including advanced user behavior tracking and predictive analytics, enhances the value proposition for both SMEs and large enterprises. The market is segmented by application (SMEs and large enterprises) and by type (cloud-based and web-based), with cloud-based solutions dominating due to their accessibility and flexibility. Competitive pressures among numerous vendors, including established players like Google Analytics, Semrush, and Ahrefs, as well as emerging niche players, drive innovation and affordability, benefiting users. Geographic distribution shows strong growth across North America and Europe, with Asia-Pacific emerging as a high-growth region. However, factors such as data privacy concerns and the increasing complexity of website analytics can act as potential restraints. Despite these challenges, the continued expansion of e-commerce and digital marketing strategies across various industries will solidify the demand for robust website traffic analysis tools. The market is expected to witness further consolidation through mergers and acquisitions, with leading players investing heavily in research and development to enhance their offerings. The increasing need for real-time data analysis and integration with other marketing automation platforms will further shape market evolution. The emergence of AI-powered analytics, providing predictive insights and automated reporting, is transforming the industry and will continue to drive market expansion in the coming years. This makes this market an attractive landscape for investors and technology providers looking for strong future growth.

  9. W

    Customs and Border Protection Network Web Trends Server Master Dataset

    • cloud.csiss.gmu.edu
    Updated Mar 6, 2021
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    United States (2021). Customs and Border Protection Network Web Trends Server Master Dataset [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/customs-and-border-protection-network-web-trends-server-master-dataset
    Explore at:
    Dataset updated
    Mar 6, 2021
    Dataset provided by
    United States
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    The CBPnet Web Trends Server is a COTS report generation product uses proprietary data storage and standard web server logs as input and supports the Office of Public Affairs in providing advanced reports for web traffic analysis for CBPnet and related web sites. It utilizes product specific database to support it's functionality.

  10. d

    Swash Web Browsing Clickstream Data - 1.5M Worldwide Users - GDPR Compliant

    • datarade.ai
    .csv, .xls
    Updated Jun 27, 2023
    + more versions
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    Swash (2023). Swash Web Browsing Clickstream Data - 1.5M Worldwide Users - GDPR Compliant [Dataset]. https://datarade.ai/data-products/swash-blockchain-bitcoin-and-web3-enthusiasts-swash
    Explore at:
    .csv, .xlsAvailable download formats
    Dataset updated
    Jun 27, 2023
    Dataset authored and provided by
    Swash
    Area covered
    Jordan, Latvia, Uzbekistan, Saint Vincent and the Grenadines, Belarus, Jamaica, India, Monaco, Liechtenstein, Russian Federation
    Description

    Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

    Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

    User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

    Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

    GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

    Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

    High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

    Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

    Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

  11. d

    NYC.gov Web Analytics

    • catalog.data.gov
    • data.cityofnewyork.us
    • +3more
    Updated Sep 30, 2022
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    data.cityofnewyork.us (2022). NYC.gov Web Analytics [Dataset]. https://catalog.data.gov/dataset/nyc-gov-web-analytics
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    Dataset updated
    Sep 30, 2022
    Dataset provided by
    data.cityofnewyork.us
    Area covered
    New York
    Description

    Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.

  12. d

    Open Data Website Traffic

    • catalog.data.gov
    • data.lacity.org
    Updated Jun 21, 2025
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    data.lacity.org (2025). Open Data Website Traffic [Dataset]. https://catalog.data.gov/dataset/open-data-website-traffic
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    Dataset updated
    Jun 21, 2025
    Dataset provided by
    data.lacity.org
    Description

    Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

  13. Network Traffic Analysis NTA Software Market Report | Global Forecast From...

    • dataintelo.com
    csv, pdf, pptx
    Updated Dec 4, 2024
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    Dataintelo (2024). Network Traffic Analysis NTA Software Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-network-traffic-analysis-nta-software-market
    Explore at:
    pptx, pdf, csvAvailable download formats
    Dataset updated
    Dec 4, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Network Traffic Analysis (NTA) Software Market Outlook



    The global Network Traffic Analysis (NTA) Software market size is poised to witness a robust growth trajectory, with a projected market valuation rising from approximately USD 3.5 billion in 2023 to an impressive USD 12.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.2% during the forecast period. The surge in this market is predominantly fueled by the increasing need for sophisticated cybersecurity measures due to the escalating frequency and complexity of cyber threats. Organizations are progressively recognizing the critical importance of NTA software in detecting, monitoring, and responding to potential network anomalies and threats, driving the market's expansion.



    A major growth factor contributing to the burgeoning NTA Software market is the exponential growth in data traffic, attributed to the widespread adoption of cloud computing, IoT devices, and the ongoing digital transformation across industries. As enterprises expand their digital footprint, the volume of data traversing networks has seen an unprecedented rise, necessitating advanced network traffic analysis solutions to ensure efficient management and security of data. Moreover, the increasing sophistication of cyber threats, including advanced persistent threats (APTs) and ransomware, has made continuous network monitoring and analysis indispensable for organizations striving to protect sensitive information and maintain business continuity.



    Another significant driver for the NTA Software market is the growing regulatory pressures and compliance requirements across various sectors, including BFSI, healthcare, and government. These regulations mandate organizations to implement robust cybersecurity frameworks and ensure data protection, thereby propelling the demand for comprehensive NTA solutions. Companies are increasingly investing in NTA software to comply with standards such as GDPR, HIPAA, and PCI-DSS, which emphasize the importance of network security and data privacy. As regulatory landscapes continue to evolve, the necessity for effective network traffic analysis tools becomes even more pronounced, further accelerating market growth.



    The increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in network traffic analysis is also a key factor driving the market's growth. These technologies enhance the capabilities of NTA software by enabling automated threat detection, predictive analytics, and anomaly detection, thereby improving the overall efficiency and accuracy of network monitoring. The integration of AI and ML has allowed NTA solutions to evolve from traditional reactive systems to proactive security platforms, capable of identifying and mitigating threats in real-time. This technological advancement is particularly attractive to large enterprises and government agencies that require robust security measures to safeguard critical infrastructure and data.



    From a regional perspective, North America is anticipated to lead the NTA Software market during the forecast period, owing to the region's well-established IT infrastructure and the presence of major industry players. The Asia Pacific region, however, is expected to witness the fastest growth, driven by rapid technological advancements, increasing internet penetration, and a rising focus on cybersecurity across emerging economies such as India and China. Europe also presents significant growth opportunities, supported by stringent data protection regulations and growing investments in cybersecurity solutions. These regional dynamics highlight the diverse growth trajectories and opportunities present across the global NTA Software market.



    Component Analysis



    The Network Traffic Analysis Software market is segmented into two primary components: software and services. The software segment accounts for the largest share of the market and is expected to continue its dominance throughout the forecast period. This is primarily due to the increasing demand for advanced network traffic analysis solutions that can efficiently monitor, detect, and respond to potential security threats. With the escalating frequency of cyberattacks, organizations are increasingly leveraging sophisticated software to enhance their network security posture and mitigate risks. The software component includes various solutions such as real-time traffic monitoring, anomaly detection, and threat intelligence, which are integral to comprehensive network security strategies.



    The services segment, on the other hand, is projected to witness signi

  14. Global Network Traffic Analytics Market 2018-2022

    • technavio.com
    Updated Jun 21, 2018
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    Technavio (2018). Global Network Traffic Analytics Market 2018-2022 [Dataset]. https://www.technavio.com/report/global-network-traffic-analytics-market-analysis-share-2018
    Explore at:
    Dataset updated
    Jun 21, 2018
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global
    Description

    Snapshot img

    Global network traffic analytics Industry Overview

    Technavio’s analysts have identified the increasing use of network traffic analytics solutions to be one of major factors driving market growth. With the rapidly changing IT infrastructure, security hackers can steal valuable information through various modes. With the increasing dependence on web applications and websites for day-to-day activities and financial transactions, the instances of theft have increased globally. Also, the emergence of social networking websites has aided the malicious attackers to extract valuable information from vulnerable users. The increasing consumer dependence on web applications and websites for day-to-day activities and financial transactions are further increasing the risks of theft. This encourages the organizations to adopt network traffic analytics solutions.

    Want a bigger picture? Try a FREE sample of this report now!

    See the complete table of contents and list of exhibits, as well as selected illustrations and example pages from this report.

    Companies covered

    The network traffic analytics market is fairly concentrated due to the presence of few established companies offering innovative and differentiated software and services. By offering a complete analysis of the competitiveness of the players in the network monitoring tools market offering varied software and services, this network traffic analytics industry analysis report will aid clients identify new growth opportunities and design new growth strategies.

    The report offers a complete analysis of a number of companies including:

    Allot
    Cisco Systems
    IBM
    Juniper Networks
    Microsoft
    Symantec
    

    Network traffic analytics market growth based on geographic regions

    Americas
    APAC
    EMEA
    

    With a complete study of the growth opportunities for the companies across regions such as the Americas, APAC, and EMEA, our industry research analysts have estimated that countries in the Americas will contribute significantly to the growth of the network monitoring tools market throughout the predicted period.

    Network traffic analytics market growth based on end-user

    Telecom
    BFSI
    Healthcare
    Media and entertainment
    

    According to our market research experts, the telecom end-user industry will be the major end-user of the network monitoring tools market throughout the forecast period. Factors such as increasing use of network traffic analytics solutions and increasing use of mobile devices at workplaces will contribute to the growth of the market shares of the telecom industry in the network traffic analytics market.

    Key highlights of the global network traffic analytics market for the forecast years 2018-2022:

    CAGR of the market during the forecast period 2018-2022
    Detailed information on factors that will accelerate the growth of the network traffic analytics market during the next five years
    Precise estimation of the global network traffic analytics market size and its contribution to the parent market
    Accurate predictions on upcoming trends and changes in consumer behavior
    Growth of the network traffic analytics industry across various geographies such as the Americas, APAC, and EMEA
    A thorough analysis of the market’s competitive landscape and detailed information on several vendors
    Comprehensive information about factors that will challenge the growth of network traffic analytics companies
    

    Get more value with Technavio’s INSIGHTS subscription platform! Gain easy access to all of Technavio’s reports, along with on-demand services. Try the demo

    This market research report analyzes the market outlook and provides a list of key trends, drivers, and challenges that are anticipated to impact the global network traffic analytics market and its stakeholders over the forecast years.

    The global network traffic analytics market analysts at Technavio have also considered how the performance of other related markets in the vertical will impact the size of this market till 2022. Some of the markets most likely to influence the growth of the network traffic analytics market over the coming years are the Global Network as a Service Market and the Global Data Analytics Outsourcing Market.

    Technavio’s collection of market research reports offer insights into the growth of markets across various industries. Additionally, we also provide customized reports based on the specific requirement of our clients.

  15. Network Traffic Analysis Solutions Market by Deployment Type, User Type,...

    • futuremarketinsights.com
    pdf
    Updated May 6, 2022
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    Future Market Insights (2022). Network Traffic Analysis Solutions Market by Deployment Type, User Type, Industry Vertical & Region | Forecast 2022 to 2032 [Dataset]. https://www.futuremarketinsights.com/reports/network-traffic-analysis-solutions-market
    Explore at:
    pdfAvailable download formats
    Dataset updated
    May 6, 2022
    Dataset authored and provided by
    Future Market Insights
    License

    https://www.futuremarketinsights.com/privacy-policyhttps://www.futuremarketinsights.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Worldwide
    Description

    [309 Pages Report] The network traffic analysis solution market is expected to expand its roots in the global market at a moderate CAGR of 11.3% through 2032.

    AttributesDetails
    Network Traffic Analysis Solutions Market CAGR (2022 to 2032)11.3%
    Network Traffic Analysis Solutions Market Value (2022)US$ 2.9 Billion
    Network Traffic Analysis Solutions Market Value (2032)US$ 8.5 Billion

    What is the Regional Analysis for the Network Traffic Analysis Solutions Market?

    RegionsCAGR (2022 to 2032)
    United States of America12.3%
    United Kingdom12.3%
    China14.9%
    Japan13.8%
    India13.6%
  16. i

    ASNM Datasets: A Collection of Network Traffic Data for Testing of...

    • ieee-dataport.org
    Updated May 18, 2022
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    Ivan Homoliak (2022). ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors [Dataset]. https://ieee-dataport.org/open-access/asnm-datasets-collection-network-traffic-data-testing-adversarial-classifiers-and
    Explore at:
    Dataset updated
    May 18, 2022
    Authors
    Ivan Homoliak
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ASNM datasets include records consisting of many features

  17. LoRaWAN Traffic Analysis Dataset

    • zenodo.org
    zip
    Updated Aug 28, 2023
    + more versions
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    Ales Povalac; Ales Povalac; Jan Kral; Jan Kral (2023). LoRaWAN Traffic Analysis Dataset [Dataset]. http://doi.org/10.5281/zenodo.7919213
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 28, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Ales Povalac; Ales Povalac; Jan Kral; Jan Kral
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset was created by a LoRaWAN sniffer and contains packets, which are thoroughly analyzed in the paper Exploring LoRaWAN Traffic: In-Depth Analysis of IoT Network Communications (not yet published). Data from the LoRaWAN sniffer was collected in four cities: Liege (Belgium), Graz (Austria), Vienna (Austria), and Brno (Czechia).

    Gateway ID: b827ebafac000001

    • Uplink reception (end-device => gateway)
    • Only packets containing CRC, inverted IQ
    • RX0: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz - BW 125 kHz and all SF
    • RX1: 868.1 MHz, 868.3 MHz, 868.5 MHz - BW 125 kHz and all SF

    Gateway ID: b827ebafac000002

    • Downlink reception (gateway => end-device)
    • Includes packets without CRC, non-inverted IQ
    • RX0: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz - BW 125 kHz and all SF
    • RX1: 868.1 MHz, 868.3 MHz, 868.5 MHz - BW 125 kHz and all SF

    Gateway ID: b827ebafac000003

    • Downlink reception (gateway => end-device) and Class-B beacon on 869.525 MHz
    • Includes packets without CRC, non-inverted IQ
    • RX0: 869.525 MHz - BW 125 kHz and all SF, BW 125 kHz and SF9 with implicit header, CR 4/5 and length 17 B

    To open the pcap files, you need Wireshark with current support for LoRaTap and LoRaWAN protocols. This support will be available in the official 4.1.0 release. A working version for Windows is accessible in the automated build system.

    The source data is available in the log.zip file, which contains the complete dataset obtained by the sniffer. A set of conversion tools for log processing is available on Github. The converted logs, available in Wireshark format, are stored in pcap.zip. For the LoRaWAN decoder, you can use the attached root and session keys. The processed outputs are stored in csv.zip, and graphical statistics are available in png.zip.

    This data represents a unique, geographically identifiable selection from the full log, cleaned of any errors. The records from Brno include communication between the gateway and a node with known keys.

    Test file :: 00_Test

    • short test file for parser verification
    • comparison of LoRaTap version 0 and version 1 formats

    Brno, Czech Republic :: 01_Brno

    • 49.22685N, 16.57536E, ASL 306m
    • lines 150873 to 529796
    • time 1.8.2022 15:04:28 to 17.8.2022 13:05:32
    • preliminary experiment
    • experimental device
      • Device EUI: 70b3d5cee0000042
      • Application key: d494d49a7b4053302bdcf96f1defa65a
      • Device address: 00d85395
      • Network session key: c417540b8b2afad8930c82fcf7ea54bb
      • Application session key: 421fea9bedd2cc497f63303edf5adf8e

    Liege, Belgium :: 02_Liege :: evaluated in the paper

    • 50.66445N, 5.59276E, ASL 151m
    • lines 636205 to 886868
    • time 25.8.2022 10:12:24 to 12.9.2022 06:20:48

    Brno, Czech Republic :: 03_Brno_join

    • 49.22685N, 16.57536E, ASL 306m
    • lines 947787 to 979382
    • time 30.9.2022 15:21:27 to 4.10.2022 10:46:31
    • record contains OTAA activation (Join Request / Join Accept)
    • experimental device:
      • Device EUI: 70b3d5cee0000042
      • Application key: d494d49a7b4053302bdcf96f1defa65a
      • Device address: 01e65ddc
      • Network session key: e2898779a03de59e2317b149abf00238
      • Application session key: 59ca1ac91922887093bc7b236bd1b07f

    Graz, Austria :: 04_Graz :: evaluated in the paper

    • 47.07049N, 15.44506E, ASL 364m
    • lines 1015139 to 1178855
    • time 26.10.2022 06:21:07 to 29.11.2022 10:03:00

    Vienna, Austria :: 05_Wien :: evaluated in the paper

    • 48.19666N, 16.37101E, ASL 204m
    • lines 1179308 to 3657105
    • time 1.12.2022 10:42:19 to 4.1.2023 14:00:05
    • contains a total of 14 short restarts (under 90 seconds)

    Brno, Czech Republic :: 07_Brno :: evaluated in the paper

    • 49.22685N, 16.57536E, ASL 306m
    • lines 4969648 to 6919392
    • time 16.2.2023 8:53:43 to 30.3.2023 9:00:11
  18. N

    Network Traffic Analysis Technology (NTA) Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Feb 18, 2025
    + more versions
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    Archive Market Research (2025). Network Traffic Analysis Technology (NTA) Report [Dataset]. https://www.archivemarketresearch.com/reports/network-traffic-analysis-technology-nta-35587
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    Feb 18, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global Network Traffic Analysis Technology (NTA) market is expanding rapidly, driven by the increasing need for network visibility and security. In 2025, the market was valued at XXX million and is projected to reach XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The market is primarily driven by the growing adoption of cloud-based services, the proliferation of IoT devices, and the increasing sophistication of cyber threats. Large enterprises, in particular, are investing heavily in NTA solutions to gain insights into their network traffic patterns and identify potential threats. The NTA market is segmented by type (on-premise, cloud-based), application (large enterprise, small and medium enterprise), and region. The cloud-based segment is expected to witness the highest growth rate during the forecast period, as enterprises increasingly adopt cloud-based services to reduce IT infrastructure costs and improve agility. The large enterprise segment is currently the largest segment in the market and is expected to continue to dominate throughout the forecast period. North America and Europe are the largest regional markets, followed by Asia Pacific. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapidly growing IT infrastructure in the region.

  19. N

    Network Traffic Analysis Market Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 3, 2025
    + more versions
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    Data Insights Market (2025). Network Traffic Analysis Market Report [Dataset]. https://www.datainsightsmarket.com/reports/network-traffic-analysis-market-13697
    Explore at:
    ppt, pdf, docAvailable download formats
    Dataset updated
    Mar 3, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Network Traffic Analysis (NTA) market is experiencing robust growth, projected to reach $3.56 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.78% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing complexity of network infrastructure, coupled with the proliferation of cloud-based applications and the rise of cyber threats, necessitates sophisticated NTA solutions for enhanced security and performance monitoring. Organizations across various sectors, including BFSI (Banking, Financial Services, and Insurance), IT and Telecom, Government, Energy and Power, and Retail, are adopting NTA to gain better visibility into their network traffic, identify anomalies, and proactively address potential issues. The shift towards cloud-based deployment models is further accelerating market growth, offering scalability, flexibility, and reduced infrastructure costs. Competitive innovation within the NTA space, characterized by the development of AI-powered analytics and automation capabilities, is also contributing to this positive trajectory. However, certain restraints are impacting market growth. The high initial investment cost associated with implementing NTA solutions, particularly for smaller organizations, can be a barrier to entry. Furthermore, the need for skilled professionals to effectively manage and interpret NTA data poses a challenge. Despite these challenges, the long-term growth prospects for the NTA market remain strong. The increasing reliance on network connectivity across all aspects of business and the evolving threat landscape will continue to drive demand for advanced NTA solutions throughout the forecast period. The market is segmented by deployment (on-premise and cloud-based) and end-user vertical, with the cloud-based segment expected to show higher growth due to its inherent advantages. This comprehensive report provides an in-depth analysis of the global Network Traffic Analysis (NTA) market, covering the period from 2019 to 2033. It offers valuable insights into market size, growth drivers, emerging trends, challenges, and key players, utilizing data from the base year 2025 and forecasting until 2033. The report is essential for businesses, investors, and researchers seeking a thorough understanding of this dynamic market segment. Key search terms addressed include: Network Traffic Analysis, NTA Market, Network Security, Cybersecurity, Cloud-based NTA, On-premise NTA, Network Monitoring, Data Analytics, and more. Recent developments include: September 2022: AlphaSOC Inc., a security analytics company, introduced its AlphaSOC Analytics Engine (AE) solution, a cloud-native NTA product that identifies the compromised workloads across Google Cloud Platform, Microsoft Azure, and Amazon Web Services., April 2022: Palo Alto Networks launched a product called Okyo Garde Enterprise Edition, which has been designed to provide lateral migration by isolating the company network from the employee's network at home. It would also protect unmanaged work equipment at home, such as hardware prototypes, printers, and VoIP phones.. Key drivers for this market are: Emergence of Network Traffic Analysis as the Key to Cyber Security, Increasing Demand for Higher Access Speed. Potential restraints include: Growing Threat of Video Content Piracy and Security Threat of User Database Due to Spyware. Notable trends are: BFSI Sector is Expected to Hold a Significant Market Share.

  20. t

    Network Traffic Analysis Solutions Global Market Report 2025

    • thebusinessresearchcompany.com
    pdf,excel,csv,ppt
    Updated Jan 21, 2025
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    The Business Research Company (2025). Network Traffic Analysis Solutions Global Market Report 2025 [Dataset]. https://www.thebusinessresearchcompany.com/report/network-traffic-analysis-solutions-global-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Jan 21, 2025
    Dataset authored and provided by
    The Business Research Company
    License

    https://www.thebusinessresearchcompany.com/privacy-policyhttps://www.thebusinessresearchcompany.com/privacy-policy

    Description

    Global Network Traffic Analysis Solutions market size is expected to reach $10.07 billion by 2029 at 13.7%, segmented as by solution, network traffic analysis software, network traffic analysis appliances

Share
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data.brla.gov (2025). Website Analytics [Dataset]. https://catalog.data.gov/dataset/website-analytics-89ba5

Website Analytics

Explore at:
Dataset updated
Jun 29, 2025
Dataset provided by
data.brla.gov
Description

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

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