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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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.

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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

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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.

Traffic analytics, rankings, and competitive metrics for free-ebooks.net as of August 2025

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

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.

At Echo, our dedication to data curation is unmatched; we focus on providing our clients with an in-depth picture of a physical location based on activity in and around a point of interest over time. Our dataset empowers you to explore the “what” by allowing you to dig deeper into customer movement behaviors, eliminate gaps in your trade area and discover untapped potential. Leverage Echo's Activity datasets to identify new growth opportunities and gain a competitive advantage.

This sample of our Area Activity data provides you insights into the estimated total unique visitors and visits in an area. This helps you understand frequentation dynamics over time, identify emerging trends in people movements and measure the impact of external factors on how people move across a city.

Additional Information: - Understand the actual movement patterns of consumers without using PII data, gaining a 360-degree consumer view. Complement your online behavior knowledge with actual offline actions, and better attribute intent based on real-world behaviors. - Echo collects, cleans and updates its footfall on a daily basis. Normalization of the data occurs on a monthly basis. - We provide data aggregation on a weekly, monthly and quarterly basis. - Information about our country offering and data schema can be found here:

1) Data Schema: https://docs.echo-analytics.com/activity/data-schema
2) Country Availability: https://docs.echo-analytics.com/activity/country-coverage
3) Methodology: https://docs.echo-analytics.com/activity/methodology

Echo's commitment to customer service is evident in our exceptional data quality and dedicated team, providing 360° support throughout your location intelligence journey. We handle the complex tasks to deliver analysis-ready datasets to you.

Business Needs: 1. Site Selection: Leverage footfall data to identify the best location to open a new store. By analyzing areas with high footfall you can select sites that are likely to attract more customers. 2. Urban Planning Development: City planners can use footfall data to optimize the layout and infrastructure of urban areas, guide the development of commercial areas by indicating where pedestrian traffic is heaviest, and aid in traffic management and safety measures. 3. Real Estate Investment: Leverage footfall data to identify lucrative investment opportunities and optimize property management by analyzing pedestrian traffic patterns.

Computer Vision Traffic Analytics Market Outlook

According to our latest research, the global Computer Vision Traffic Analytics market size reached USD 5.7 billion in 2024, reflecting robust growth driven by rapid urbanization and the increasing need for intelligent traffic management systems. The market is expected to expand at a CAGR of 18.3% from 2025 to 2033, projecting a significant rise to USD 29.1 billion by 2033. The primary growth factor fueling this surge is the integration of artificial intelligence and machine learning into computer vision platforms, enabling real-time data processing and advanced analytics for urban mobility solutions.

One of the most significant growth drivers for the Computer Vision Traffic Analytics market is the escalating demand for smart city solutions worldwide. Governments and municipal authorities are increasingly investing in intelligent transportation systems to enhance urban mobility, reduce congestion, and improve public safety. The proliferation of high-definition surveillance cameras and IoT sensors across road networks provides a rich data source for computer vision algorithms, enabling more precise traffic monitoring, incident detection, and flow optimization. These advancements allow for dynamic traffic signal adjustments, real-time congestion alerts, and efficient emergency response, thereby reducing travel time and improving overall commuter experience. The integration of computer vision technology into existing infrastructure is also cost-effective compared to traditional expansion methods, making it a preferred choice for urban planners.

Another critical factor propelling market growth is the rapid advancement in deep learning and neural network architectures, which have dramatically improved the accuracy and reliability of traffic analytics. Modern computer vision systems can now process vast volumes of video and sensor data in real time, identifying patterns such as vehicle density, speed violations, and pedestrian movement with high precision. This capability is particularly valuable for applications like license plate recognition, automated tolling, and pedestrian analysis, which require instantaneous and error-free data interpretation. The evolution of edge computing further enhances these systems by enabling data processing closer to the source, reducing latency and bandwidth requirements. This technological leap is fostering widespread adoption across both developed and emerging markets, as cities seek to leverage data-driven insights for smarter, safer transportation networks.

The rising focus on sustainability and environmental management is also shaping the Computer Vision Traffic Analytics market. With increasing awareness of the environmental impact of vehicular emissions and traffic congestion, city authorities are turning to intelligent analytics platforms to optimize traffic flow and minimize idle times at intersections. By leveraging real-time data, these systems can dynamically reroute traffic, prioritize public transport, and manage peak-hour congestion, leading to reduced carbon footprints and better air quality. Additionally, the integration of computer vision traffic analytics with broader urban mobility platforms supports the development of multimodal transport strategies, encouraging the use of bicycles, e-scooters, and public transit. This holistic approach aligns with global sustainability goals and positions computer vision as a cornerstone technology in the future of urban transportation.

From a regional perspective, North America currently leads the global market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The dominance of these regions can be attributed to early adoption of smart city initiatives, substantial investments in infrastructure modernization, and the presence of leading technology providers. However, the Asia Pacific region is anticipated to witness the fastest growth over the forecast period, driven by rapid urbanization, government-led smart city projects, and increasing vehicle ownership rates. Emerging economies in Latin America and the Middle East & Africa are also expected to contribute significantly to market expansion as they embrace digital transformation in transportation management. This diverse regional landscape underscores the universal relevance and scalability of computer vision traffic analytics solutions.

Average traffic speed and total journey time of driving route based on traffic data from Traffic Data Analytics System.

This is an urban traffic speed dataset which consists of 214 anonymous road segments (mainly consist of urban expressways and arterials) within two months (i.e., 61 days from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected in Guangzhou, China. In practice, it can be used to conduct missing data imputation, short-term traffic prediction, and traffic pattern discovery experiments.

According to the spatial and temporal attributes, we can easily derive a third-order tensor as \(\mathcal{X}\in\mathbb{R}^{214\times 61\times 144}\) and its dimensions include road segment, day and time window (see the file tensor.mat). The total number of speed observations (or non-zero entries of the tensor \(\mathcal{X}\)) is \(1,855,589\). If the dataset is complete, then we have \(214\times 61\times 144=1,879,776\) observations, therefore, the original missing rate of this dataset is \(1.29\%\).

Note that the file traffic_speed_data.csv is the original traffic speed data with four columns including road segment attribute, day attribute, time window attribute, and traffic speed value. The file day_information_table.csv is a table referring to the specific date, and the file time_information_table.csv is a table expressing time window with start time and end time information.

Feel free to email me with any questions: chenxy346@mail2.sysu.edu.cn (author: Xinyu Chen).

Acknowledgement: Mr. Weiwei Sun (affiliated with Sun Yat-Sen University) also provided insightful suggestion and help for publishing this data set. Thank you!

The global Clickstream Analytics Market was valued at $615.37 Million in 2022, and is projected to $1,298.63 Million by 2030, growing at a CAGR of 11.26%.

This foot traffic dataset provides GPS-based mobile movement signals from across South America. It is ideal for retailers, city agencies, advertisers, and real estate professionals seeking insights into how people move through physical locations and urban spaces.

Each record includes:

Device ID (IDFA or GAID) Timestamps (in milliseconds and readable format) GPS coordinates (lat/lon) Country code Horizontal accuracy (85%) Optional IP address, mobile carrier, and device model

Access the data via polygon queries (up to 10,000 tiles), and receive files in CSV, JSON, or Parquet, delivered hourly or daily via API, AWS S3, or Google Cloud. Data freshness is strong (95% delivered within 3 days), with full historical backfill available from September 2024.

This solution supports flexible credit-based pricing and is privacy-compliant under GDPR and CCPA.

Key Attributes:

Custom POI or polygon query capability Backfilled GPS traffic available across LATAM High-resolution movement with daily/hourly cadence GDPR/CCPA-aligned with opt-out handling Delivery via API or major cloud platforms

Use Cases:

Competitive benchmarking across malls or stores Transport and infrastructure planning Advertising attribution for outdoor/DOOH campaigns Footfall modeling for commercial leases City zoning, tourism, and planning investments Telecom & tower planning across developing corridors

free-qr.com is ranked #9316 in TR with 381.36K Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

free.nf is ranked #12179 in IN with 1.73M Traffic. Categories: . Learn more about website traffic, market share, and more!

Research data on traffic exchange limitations including low-quality traffic characteristics, search engine penalty risks, and comparison with effective alternatives like SEO and content marketing strategies.

shahed4u.free is ranked #17232 in EG with 58.01K Traffic. Categories: . Learn more about website traffic, market share, and more!

Encrypted Traffic Analytics Platform Market Outlook

According to our latest research, the global Encrypted Traffic Analytics Platform market size reached USD 1.85 billion in 2024, reflecting robust momentum driven by the escalating demand for advanced cybersecurity solutions. The market is projected to expand at a compelling CAGR of 21.7% from 2025 to 2033, with the market size expected to reach USD 13.2 billion by 2033. This rapid growth is primarily propelled by an upsurge in encrypted network traffic across organizations, necessitating sophisticated analytics platforms to ensure secure, compliant, and efficient operations in an increasingly digitalized world.

The exponential growth in the Encrypted Traffic Analytics Platform market is fundamentally attributed to the proliferation of encrypted data flows within enterprise networks. As organizations adopt end-to-end encryption to safeguard sensitive information and comply with stringent data protection regulations, visibility into network traffic diminishes, thereby complicating threat detection and response. Encrypted Traffic Analytics Platforms address this challenge by leveraging machine learning, behavioral analytics, and advanced decryption techniques to identify anomalies, malware, and potential breaches without compromising privacy. The surge in sophisticated cyber threats, such as ransomware and advanced persistent threats (APTs), has further accelerated the demand for these platforms, as traditional security tools often struggle to inspect encrypted payloads effectively.

Another significant driver fueling the expansion of the Encrypted Traffic Analytics Platform market is the widespread digital transformation initiatives undertaken by enterprises worldwide. With the migration of critical workloads to cloud environments, the adoption of remote work models, and the integration of IoT devices, the volume and complexity of encrypted network traffic have surged dramatically. This evolving digital landscape has heightened the need for real-time, scalable analytics solutions capable of providing actionable insights into encrypted communications. Additionally, regulatory frameworks such as GDPR, HIPAA, and PCI DSS mandate rigorous monitoring and protection of data in transit, compelling organizations across sectors like BFSI, healthcare, and government to invest heavily in encrypted traffic analytics technologies.

The rapid evolution of artificial intelligence and machine learning technologies has also played a pivotal role in shaping the Encrypted Traffic Analytics Platform market. Modern analytics platforms are increasingly incorporating AI-driven algorithms to enhance the accuracy and efficiency of threat detection within encrypted traffic streams. These advancements enable organizations to automate the identification of suspicious patterns, reduce false positives, and streamline incident response processes. Furthermore, the growing ecosystem of cybersecurity vendors and the rise of managed security services have expanded the accessibility of encrypted traffic analytics solutions to small and medium enterprises, democratizing advanced threat intelligence capabilities across the market.

Regionally, North America continues to dominate the Encrypted Traffic Analytics Platform market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading cybersecurity vendors, high adoption rates of advanced digital technologies, and stringent regulatory mandates have established North America as a hub for innovation and investment in encrypted traffic analytics. Meanwhile, Asia Pacific is poised for the fastest growth over the forecast period, driven by the rapid digitization of emerging economies, escalating cyber threats, and increasing awareness of the importance of encrypted traffic analysis for data protection and compliance.

Component Analysis

The Encrypted Traffic Analytics Platform market, segmented by component into software, hardware, and services, presents a dynam

Encrypted Traffic Analytics Market Outlook

According to our latest research, the encrypted traffic analytics market size reached USD 1.87 billion in 2024, with robust momentum driven by escalating cyber threats and the increasing volume of encrypted network traffic worldwide. The market is projected to grow at a compound annual growth rate (CAGR) of 20.5% from 2025 to 2033, reaching an estimated USD 11.41 billion by 2033. This rapid expansion is fueled by the urgent need for advanced network security solutions capable of analyzing encrypted data flows without compromising privacy or performance, as organizations across all sectors strive to mitigate sophisticated cyberattacks and maintain regulatory compliance in an increasingly digitalized global economy.

One of the primary growth factors driving the encrypted traffic analytics market is the exponential surge in encrypted data traversing enterprise networks. As organizations adopt cloud-based applications, remote work models, and IoT devices, the majority of network traffic is now encrypted to safeguard sensitive information. While encryption enhances data privacy, it also poses significant challenges for traditional security tools, which struggle to inspect encrypted packets for threats without decryption. This challenge has pushed enterprises to invest in advanced encrypted traffic analytics solutions that leverage machine learning and behavioral analysis to detect anomalies, malware, and policy violations within encrypted streams, thereby ensuring robust threat visibility and proactive incident response without breaching data confidentiality.

Another significant growth driver is the evolving regulatory landscape and the rising emphasis on data privacy and compliance across industries. Regulations such as GDPR, HIPAA, and PCI DSS mandate stringent controls over data access, transmission, and monitoring, compelling enterprises to deploy security solutions that can analyze encrypted traffic for compliance violations and data loss prevention. Encrypted traffic analytics platforms enable organizations to audit encrypted communications, identify policy breaches, and generate compliance reports without decrypting sensitive payloads, thus supporting regulatory adherence while minimizing the risk of data exposure. This dual imperative of security and compliance is particularly pronounced in highly regulated sectors such as BFSI, healthcare, and government, where encrypted traffic analytics have become indispensable.

The proliferation of advanced persistent threats (APTs) and sophisticated malware campaigns that exploit encrypted channels to evade detection is further accelerating market growth. Cybercriminals increasingly use encrypted tunnels to bypass perimeter defenses and deliver malicious payloads, making it critical for security teams to gain visibility into encrypted traffic flows. Encrypted traffic analytics solutions, powered by AI and deep packet inspection techniques, allow organizations to detect hidden threats, lateral movement, and command-and-control communications within encrypted sessions. This proactive approach not only reduces dwell time and limits the impact of breaches but also enhances overall network resilience in the face of evolving cyber risks.

From a regional perspective, North America continues to dominate the encrypted traffic analytics market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The region's leadership is attributed to the high concentration of technology innovators, stringent regulatory frameworks, and the early adoption of advanced cybersecurity solutions by enterprises and government agencies. Meanwhile, Asia Pacific is witnessing the fastest growth, supported by rapid digital transformation, increasing cyberattacks, and rising investments in IT security infrastructure across emerging economies. Europe also remains a key market, driven by robust data protection laws and growing awareness of encrypted threat vectors among organizations of all sizes.

Component Analysis

Representative applications that can directly collect 5G da-tasets from mobile terminals without using specialized equipment include G-NetTrack Pro and PCAPdroid. The for-mer allows for the monitoring and logging of the header and payload information of the medium access control (MAC) frame passing through the 5G air interface. The latter is an open-source network capture and monitoring tool that works without root privileges, analyzing connections made by ap-plications installed on the user's mobile device. The latter can also dump mobile traffic to PCAP (also known as libpcap) and send it to the well-known Wireshark for further analysis. We created 5G datasets by measuring 5G traffic directly from a major mobile operator in South Korea. The model name of the mobile terminal used for traffic measurement is the Samsung Galaxy A90 5G, and it was equipped with a Qualcomm Snapdragon X50 5G modem. The packet sniffer software used for traffic measurement, PCAPdroid, was in-stalled in the terminal through Google play. Traffic was measured sequentially per application on two stationary ter-minals (only one terminal was used for non-interactive ser-vices) with no background traffic. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G network planning and provisioning, and background traffic was not mixed to measure the unique characteristics of each type of traffic. The video streaming dataset includes data directly meas-ured while watching Netflix and Amazon Prime, which are representative over-the-top (OTT) services, on mobile devic-es. The live streaming dataset was measured while watching YouTube Live and South Korea's representative live broad-casts (Naver NOW and Afreeca TV). Video conferencing data were measured by holding an actual meeting on the widely used Zoom, MS Teams, and Google Meet platform. Two types of metaverse traffic were acquired: Zepeto and Roblox. Zepeto traffic was collected while staying in the 'camping world' for 15 hours. Roblox traffic was collected over 25 hours of playing the 'Collect All Pets' game using an auto clicker. We collected two types of mobile network gaming traffic. The first was cloud gaming, an online game setup that runs video games on remote servers and streams them direct-ly to the user's device. The second was a traditional mobile game connected to the Internet. The dataset was collected from May to October 2022, is a massive 328 hours in total, and is provided in the csv file format. The dataset we collected is a timestamp-mapped time series dataset with packet header information, and traffic analysis by application is possible because it includes source and destination addresses. To make it more usable as a traffic source model, Section III describes how to use it as a training dataset for the traffic simulator platform's source generator.

A 5G traffic dataset measured by PCAPdroid has been re-leased and can be used as a training dataset for various ML models. However, since the size of this dataset is very large, it is inconvenient to handle, and additional data preprocessing is required to use it for its intended purpose.

This data set can be used to learn GANs, time-series forcasting deep learning models.

Our implementation is given on GitHub. https://github.com/0913ktg/5G-Traffic-Generator

The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.

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} }