Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.

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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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.

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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.

The global market for visitor tracking software tools is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. The market, estimated at $5 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated market value of $12 billion by 2033. This growth is fueled by several key factors: the rising adoption of e-commerce, the increasing complexity of online marketing campaigns, the demand for personalized user experiences, and the growing availability of sophisticated analytics tools capable of providing actionable insights from website traffic data. Major trends shaping the market include the integration of AI and machine learning for predictive analytics, the increasing use of heatmaps and session recordings for detailed user behavior analysis, and a growing focus on privacy-compliant data collection methods. However, market growth faces certain restraints. Concerns around data privacy and compliance with regulations like GDPR are impacting adoption rates. Furthermore, the competitive landscape is crowded, with both established players like Google and specialized providers like Crazy Egg vying for market share. The market is segmented by solution type (e.g., website analytics, heatmap tools, session recording), deployment model (cloud-based, on-premise), enterprise size (small, medium, large), and industry vertical. Leading companies such as Crazy Egg, Mixpanel, and FullStory are constantly innovating to improve the accuracy and depth of their offerings, while smaller companies are focusing on niche functionalities to differentiate themselves. The future success of these tools depends heavily on continuing innovation in the areas of data security, user experience, and integration with other marketing platforms.

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:

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.

Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

Methodology

The data collected originates from SimilarWeb.com.

Source

For the analysis and study, go to The Concept Center

This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

How to use this dataset

• Analyze 11/1/2016 in relation to 2/1/2017
• Study the influence of 4/1/2017 on 1/1/2017
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

Dataset

This dataset was created by YUCHU Shen

Released under Apache 2.0

Contents

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.

The global web analytics market, valued at $5529.7 million in 2025, is poised for substantial growth. While the provided CAGR is missing, considering the rapid advancements in digital technologies and the increasing reliance on data-driven decision-making across industries, a conservative estimate would place the Compound Annual Growth Rate (CAGR) between 15% and 20% for the forecast period 2025-2033. This growth is fueled by several key drivers: the rising adoption of cloud-based analytics solutions, the increasing demand for real-time data insights, and the growing need for personalized customer experiences. Furthermore, the expansion of e-commerce and the proliferation of mobile devices are significantly contributing to the market's expansion. Emerging trends such as artificial intelligence (AI) and machine learning (ML) integration within web analytics platforms are further enhancing analytical capabilities and driving market growth. While challenges like data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive, suggesting a significant increase in market value by 2033. The competitive landscape is dynamic, with a mix of established players like Adobe, Google, and IBM alongside agile startups like Heap and Mouseflow. These companies offer a range of solutions catering to different business sizes and needs, from basic website traffic analysis to sophisticated predictive analytics. The market is witnessing a shift towards more user-friendly and visually appealing dashboards, making web analytics accessible to a broader range of users beyond dedicated data scientists. This democratization of data, coupled with ongoing technological advancements, promises to further accelerate market growth and consolidate the position of web analytics as a critical component of successful digital strategies across all sectors.

Click is global web traffic data combined with transaction data to enhance signal capabilities. Click adds a layer to your analysis for richer spending behavior on consumer & B2B websites, addressable market overview, trends and shopping behavior shifts, and effects of pricing on growth.

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

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Web Analytics Market Outlook

According to our latest research, the global web analytics market size was valued at USD 8.4 billion in 2024, reflecting robust growth driven by the increasing adoption of digital platforms across industries. The market is projected to expand at a compound annual growth rate (CAGR) of 17.2% from 2025 to 2033, reaching an estimated USD 36.8 billion by 2033. This significant upsurge is primarily attributed to the escalating demand for actionable insights, data-driven decision-making, and the proliferation of online consumer activity. As per the latest research, enterprises worldwide are leveraging advanced web analytics tools to enhance customer engagement, improve marketing strategies, and drive business outcomes.

One of the principal growth factors fueling the web analytics market is the exponential increase in digitalization and internet penetration. Organizations across various sectors are rapidly transitioning their operations online, resulting in a surge of data generation through multiple digital touchpoints. This digital transformation has heightened the need for sophisticated web analytics solutions that can process vast volumes of data, extract meaningful patterns, and provide actionable insights. Moreover, the rise in e-commerce activities, coupled with the growing popularity of social media platforms, has created a fertile environment for the adoption of web analytics, enabling businesses to track consumer behavior, measure campaign effectiveness, and optimize user experiences.

Another critical driver for the web analytics market is the integration of artificial intelligence (AI) and machine learning (ML) technologies. These advanced technologies are revolutionizing the way organizations analyze web data by enabling predictive analytics, real-time reporting, and personalized recommendations. AI-powered web analytics tools can automatically identify trends, anomalies, and customer preferences, empowering businesses to make data-driven decisions faster and more accurately. Furthermore, the increasing focus on omnichannel marketing strategies and the need to unify customer data across different platforms have further accelerated the demand for comprehensive web analytics solutions.

The regulatory landscape and growing emphasis on data privacy and compliance are also shaping the web analytics market. With the implementation of stringent data protection regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are compelled to adopt web analytics tools that ensure data security and privacy. This has led to the development of privacy-centric analytics platforms that offer enhanced data governance features, enabling businesses to comply with global regulatory requirements while still deriving valuable insights from web data. The ability to balance data-driven innovation with privacy considerations is becoming a key differentiator for vendors in this dynamic market.

From a regional perspective, North America continues to dominate the web analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to the presence of major technology providers, a mature digital ecosystem, and high levels of investment in analytics infrastructure. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by the rapid adoption of digital technologies, expanding internet user base, and increasing investments in e-commerce and digital marketing. The growing awareness among businesses in emerging economies about the benefits of web analytics is further propelling market growth in this region.

Component Analysis

The web analytics market by component is bifurcated into software and services, with each segment playing a pivotal role in market expansion. The software segment holds the lion’s share of the market, driven by the continuous evolution of analytics plat

Analysis of ‘Website Analytics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ecee4df3-8149-4b74-8927-428ea920b758 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Snapshot img

Web Analytics Market Size 2025-2029

The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.

What will be the Size of the Web Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

How is this Web Analytics Industry segmented?

The web analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Application

  Social media management
  Targeting and behavioral analysis
  Display advertising optimization
  Multichannel campaign analysis
  Online marketing


Component

  Solutions
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting,

Please refer to the original data article for further data description: Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.108888. We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:

W-2022-44

Uncompressed Size: 19 GB Capture Period: 31.10.2022 - 6.11.2022 Number of flows: 32.6M W-2022-45

Uncompressed Size: 25 GB Capture Period: 7.11.2022 - 13.11.2022 Number of flows: 42.6M W-2022-46

Uncompressed Size: 20 GB Capture Period: 14.11.2022 - 20.11.2022 Number of flows: 33.7M W-2022-47

Uncompressed Size: 25 GB Capture Period: 21.11.2022 - 27.11.2022 Number of flows: 44.1M CESNET-QUIC22

Uncompressed Size: 89 GB Capture Period: 31.10.2022 - 27.11.2022 Number of flows: 153M

Data description The dataset consists of network flows describing encrypted QUIC communications. Flows were created using ipfixprobe flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications. Packet Sequences Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, +1 meaning a packet sent from client to server, and -1 a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip. Flow statistics Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the PHISTS plugin documentation There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official IANA IPFIX-specified values. The FLOW_ENDREASON_OTHER field represents the forced end and lack of resources reasons. The end of flow detected reason is not considered because it is not relevant for UDP connections. Dataset structure The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the dataset-statistics folder. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:

ID: Unique identifier SRC_IP: Source IP address DST_IP: Destination IP address DST_ASN: Destination Autonomous System number SRC_PORT: Source port DST_PORT: Destination port PROTOCOL: Transport protocol QUIC_VERSION QUIC: protocol version QUIC_SNI: Server Name Indication domain QUIC_USER_AGENT: User agent string, if available in the QUIC Initial Packet TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff DURATION: Duration of the flow in seconds BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence PHIST_SRC_SIZES: Histogram of packet sizes from client to server PHIST_DST_SIZES: Histogram of packet sizes from server to client PHIST_SRC_IPT: Histogram of inter-packet times from client to server PHIST_DST_IPT: Histogram of inter-packet times from server to client APP: Web service label CATEGORY: Service category FLOW_ENDREASON_IDLE: Flow was terminated because it was idle FLOW_ENDREASON_ACTIVE: Flow was terminated because it reached the active timeout FLOW_ENDREASON_OTHER: Flow was terminated for other reasons

Link to other CESNET datasets

https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original data article:

@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }