Market Analysis for Network Traffic Analysis Tools The global Network Traffic Analysis (NTA) Tool market is projected to reach a valuation of USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The rising need to monitor and secure network traffic, coupled with increased adoption of cloud-based and hybrid network environments, is driving the market growth. Key industry players include Cisco, ExtraHop, ManageEngine, Netreo, Noction, Packetbeat, SolarWinds, and Splunk. The NTA tool market is segmented by type (cloud-based and on-premises) and application (BFSI, healthcare, government, retail, and others). Cloud-based solutions are gaining traction due to their scalability, flexibility, and cost-effectiveness. Key market trends include the integration of artificial intelligence (AI) and machine learning (ML) for real-time threat detection and advanced analytics. However, data privacy concerns and deployment costs may pose restraints. With growing demand from various industries, the Asia Pacific region is expected to witness significant growth in the NTA tool market in the coming years.

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...

The network traffic analytics market size was valued at USD 3.44 billion in 2024 and is likely to cross USD 13.2 billion by 2037, registering more than 10.9% CAGR during the forecast period i.e., between 2025-2037. North America industry is expected to account for largest revenue share of 35% by 2037, due to presence of two significant economies, including the USA and Canada.

The size and share of the market is categorized based on Type (Cloud Based, On Premises) and Application (Large Enterprises, SMEs) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This data set provides supplemental material for the publication "VITALflow: Visual Interactive Traffic Analysis with NetFlow", namely a video illustrating how the system is used and additional information on the expert evaluation that has been performed. The VITALflow system is a Visual Analytics tool for investigating NetFlow/IPFIX data for the purpose of network planning, formuating intents or analysing causes of implausible behaviour. This supplemental material does not only illustrate how the system behaves interactively, but most importantly details the pair analytics process how the evaluation of the system with network experts was performed.

The global Traffic Information Management Software market is experiencing robust growth, projected to reach $4,532.1 million in 2025. While the precise CAGR isn't provided, considering the rapid technological advancements in smart cities and increasing urbanization, a conservative estimate places the CAGR for the forecast period (2025-2033) between 8% and 12%. This growth is fueled by several key drivers. The rising adoption of smart city initiatives globally necessitates efficient traffic management solutions. Furthermore, the increasing penetration of connected vehicles and the availability of high-quality real-time data contribute to the market's expansion. Growing concerns over traffic congestion, environmental pollution, and road safety are also pushing governments and private entities to invest heavily in advanced traffic information management systems. The market is segmented by software type (Smart Signaling, Route Guidance, Traffic Analytics, Smart Surveillance) and application (Urban Traffic, Parking Management, Others). North America currently holds a significant market share due to early adoption and advanced technological infrastructure, but the Asia-Pacific region is expected to witness the fastest growth due to rapid urbanization and significant infrastructure development. While data security and integration challenges could pose some restraints, the overall market outlook remains positive, driven by continuous innovation and increasing demand for effective traffic solutions. The market's diverse applications across urban traffic management, parking optimization, and other related areas further diversify revenue streams. The competitive landscape is characterized by established players like Cisco Systems, Inc. and emerging tech companies alike, indicating a healthy balance between established expertise and innovative solutions. The ongoing research and development efforts focused on improving algorithms for route optimization, predictive analytics for traffic flow, and integrating Artificial Intelligence (AI) and Machine Learning (ML) technologies into these systems are poised to transform traffic management practices and contribute further to market growth. The long-term forecast predicts sustained growth, propelled by the inevitable expansion of smart city initiatives worldwide and the continued demand for improved traffic efficiency and safety.

Aurora:GeoStudio® is a premier geospatial analysis platform that excels in supporting foot traffic data through its sophisticated Population Dynamics® analytic. Foot traffic data encompasses information about the number of people visiting specific locations or establishments, providing deep insights into customer behavior, patterns, and trends. This data is crucial for businesses looking to understand their audience and make data-driven decisions.

Core Features:

  1.    Data Collection Methods:
  •    Passive Sensors: Aurora:GeoStudio® integrates data collected from passive sensors deployed at various locations. These devices count the number of visitors, track their movement paths, and record the duration of their visits.
  •    Mobile Devices: The platform also leverages data from mobile devices, providing additional insights into foot traffic patterns through location-based services and applications.
  2.    Population Dynamics® Analytic:
  •    Aurora:GeoStudio®’s Population Dynamics® analytic processes foot traffic data to deliver comprehensive insights. This analytic tool helps visualize and understand visitor behavior, peak visiting times, and movement trends within specific areas.
  3.    Visualization and Mapping:
  •    The platform offers advanced visualization capabilities, displaying foot traffic data on customizable maps from providers like Google, Esri, Open, and Stamen. These visualizations help users understand spatial patterns and relationships, facilitating informed decision-making.

Applications:

  1.    Customer Behavior Analysis:
  •    Businesses can analyze foot traffic data to understand customer behavior, such as the number of visitors, the duration of their visits, and the paths they take within an establishment. This information is crucial for tailoring services and improving customer satisfaction.
  2.    Store Layout Optimization:
  •    Foot traffic data helps businesses optimize store layouts by identifying high-traffic areas and bottlenecks. By understanding how customers move through a space, businesses can rearrange products and displays to enhance flow and maximize sales opportunities.
  3.    Marketing Strategy Enhancement:
  •    Aurora:GeoStudio® enables businesses to refine their marketing strategies by providing insights into peak visiting times and customer demographics. This data supports targeted marketing campaigns, ensuring promotions reach the right audience at the right time.
  4.    Operational Efficiency:
  •    Understanding foot traffic patterns allows businesses to optimize staffing levels, manage inventory more effectively, and improve overall operational efficiency. By aligning resources with actual customer demand, businesses can enhance service delivery and reduce costs.
  5.    Urban Planning and Public Spaces:
  •    Foot traffic data is invaluable for urban planners and managers of public spaces. It helps in designing public areas that accommodate pedestrian flow efficiently and ensures that amenities are accessible and well-placed.

Aurora:GeoStudio®’s support for foot traffic data through the Population Dynamics® analytic offers businesses and urban planners a powerful tool for understanding and optimizing visitor behavior. By leveraging data from sensors, cameras, and mobile devices, the platform provides detailed insights into customer movements and trends. These insights enable businesses to enhance their marketing strategies, optimize store layouts, and improve operational efficiency. For urban planners, foot traffic data facilitates the design of more effective and accessible public spaces. Aurora:GeoStudio®’s advanced features empower users to make informed decisions and achieve a comprehensive understanding of foot traffic dynamics, leading to better strategic outcomes.

Network Traffic Analysis (NTA) Software Market size was valued at USD 3.56 Billion in 2023 and is projected to reach USD 6.5 Billion by 2031, growing at a 12.78% CAGR from 2024 to 2031.

Network Traffic Analysis Software Market: Definition/ Overview

The Network Traffic Analysis (NTA) Software Market refers to the industry segment focused on developing tools and solutions designed to monitor, analyze, and secure network traffic within organizations. As businesses increasingly rely on digital networks for their operations, the need to understand and protect the flow of data becomes critical. NTA software provides essential capabilities for network visibility, threat detection, performance monitoring, and forensic analysis.

At its core, NTA software captures and inspects network traffic in real-time or retrospectively to identify patterns, anomalies, and potential security threats. These tools utilize advanced algorithms and machine learning techniques to interpret vast amounts of data, offering insights into network behavior, application performance, and security incidents. By examining packet headers and payloads, NTA software can detect unusual activity such as unauthorized access attempts, data exfiltration, malware propagation, and other suspicious behaviors.

The market for NTA software is driven by the increasing frequency and sophistication of cyber threats, regulatory requirements for data protection, and the growing complexity of IT environments. Organizations across various sectors, including finance, healthcare, government, and manufacturing, rely on NTA solutions to safeguard their networks and ensure uninterrupted operations.

Leading vendors in the NTA software market offer a range of solutions tailored to different organizational needs, from small businesses to large enterprises. These solutions often integrate with existing security information and event management (SIEM) systems, network infrastructure, and endpoint detection and response (EDR) tools to provide comprehensive visibility and protection against evolving threats.

The Network Traffic Analysis Software Market plays a crucial role in enhancing cybersecurity posture and operational efficiency by empowering organizations to proactively monitor, analyze, and respond to network incidents in real time. As the digital landscape continues to evolve, NTA software remains essential in defending against the ever-changing threat landscape and ensuring the integrity and availability of critical business networks.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

The global website analytics market, encompassing solutions for large enterprises and SMEs, is poised for significant growth. While the provided data lacks specific market size and CAGR figures, a reasonable estimation based on industry trends suggests a 2025 market size of approximately $15 billion, experiencing a compound annual growth rate (CAGR) of 12% from 2025 to 2033. This robust growth is fueled by several key drivers: the increasing reliance on data-driven decision-making across businesses, the escalating need for enhanced website performance optimization, and the growing adoption of sophisticated analytics tools offering deeper insights into user behavior and conversion rates. Market segmentation reveals strong demand across diverse analytics types, including product, traffic, and sales analytics. The competitive landscape is intensely dynamic, with established players like Google, SEMrush, and SimilarWeb vying for market share alongside emerging innovative companies like Owletter and TrendSource. These companies are constantly innovating to provide more comprehensive and user-friendly analytics platforms, leading to increased competition. This competitive pressure fosters innovation, but also necessitates strategic differentiation, focusing on specific niche markets or offering unique features to attract and retain customers. The market’s geographic distribution shows significant traction in North America and Europe, but emerging markets in Asia Pacific are also exhibiting substantial growth potential, driven by increasing internet penetration and digital transformation initiatives. While data security concerns and the complexity of implementing analytics tools present some restraints, the overall market outlook remains highly positive, promising considerable opportunities for market participants in the coming years.

The global 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. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of digital marketing strategies, the growing importance of data-driven decision-making, and the increasing sophistication of website visitor tracking tools. Cloud-based solutions dominate the market due to their scalability, accessibility, and cost-effectiveness, particularly appealing to Small and Medium-sized Enterprises (SMEs). However, large enterprises continue to invest significantly in on-premise solutions for enhanced data security and control. The market is highly competitive, with numerous established players and emerging startups offering a range of features and functionalities. Technological advancements, such as AI-powered analytics and enhanced integration with other marketing tools, are shaping the future of the market. The market's geographical distribution reflects the global digital landscape. North America, with its mature digital economy and high adoption rates, holds a significant market share. However, regions like Asia-Pacific are showing rapid growth, driven by increasing internet penetration and digitalization across various industries. Despite the overall positive outlook, challenges such as data privacy regulations and the increasing complexity of website tracking technology are influencing market dynamics. The ongoing competition among vendors necessitates continuous innovation and the development of more user-friendly and insightful tools. The future growth of the website visitor tracking software market is promising, fueled by the continuing importance of data-driven decision-making within marketing and business strategies. A key factor will be the ongoing adaptation to evolving privacy regulations and user expectations.

Traffic Video Analysis

## Overview

Traffic Video Analysis is a dataset for object detection tasks - it contains Traffic annotations for 4,821 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The global AI people counter market is experiencing robust growth, driven by the increasing need for accurate and real-time foot traffic analytics across various sectors. The market's expansion is fueled by several key factors, including the rising adoption of smart technologies in retail, the growing demand for optimized operational efficiency in public spaces, and the increasing need for data-driven decision-making in businesses. Computer vision-based systems are currently dominating the market due to their advanced capabilities in recognizing and tracking individuals, providing valuable insights into customer behavior and store performance. However, infrared-based systems are gaining traction due to their cost-effectiveness and ability to function effectively even in low-light conditions. The retail and office building segments are major contributors to the market's revenue, but significant growth potential exists in public transportation and other emerging sectors like smart cities and event management. Geographical distribution reveals strong market penetration in North America and Europe, driven by early adoption of technology and a developed infrastructure. However, Asia-Pacific is projected to witness significant growth over the forecast period due to rapid urbanization and increasing investments in smart infrastructure projects. While factors like the high initial investment cost and potential privacy concerns pose challenges, the overall market outlook remains positive, with a projected steady compound annual growth rate (CAGR) contributing to substantial market expansion in the coming years. The competitive landscape is characterized by a mix of established players and emerging technology providers. Established companies like Hikvision and Vivotek leverage their expertise in video surveillance to integrate AI-powered people counting solutions. Meanwhile, specialized AI startups such as Plugger AI and Dragonfruit AI are focusing on innovative solutions and customized applications, catering to niche market needs. The market is witnessing strategic partnerships and collaborations between technology providers and system integrators to expand market reach and offer comprehensive solutions. Future growth will depend on further advancements in AI algorithms, improved data analytics capabilities, enhanced integration with other business intelligence systems, and addressing privacy concerns through robust data security measures. The adoption of cloud-based solutions and the development of advanced analytics tools offering predictive insights will further fuel market growth.

The 3D people counter market is experiencing robust growth, driven by the increasing need for accurate and real-time foot traffic analysis across various sectors. Retail businesses leverage this technology to optimize store layouts, staffing levels, and marketing campaigns based on precise customer flow data. Similarly, commercial real estate utilizes 3D people counters to understand space utilization and improve operational efficiency. The market's expansion is fueled by advancements in sensor technology, providing more accurate and reliable data, and the integration of people counting systems with other analytics platforms for comprehensive insights. Wireless systems are gaining traction due to their ease of installation and flexibility, while wired systems continue to be prevalent in high-security environments demanding reliable data transmission. The market is segmented by application (residential, commercial) and type (wired, wireless), with the commercial segment currently dominating due to higher adoption rates. North America and Europe represent significant market shares, driven by early adoption and technological advancements. However, Asia-Pacific is expected to witness substantial growth in the coming years, fueled by rising urbanization and increasing retail infrastructure development. While the initial investment cost can be a restraint, the long-term return on investment (ROI) through improved operational efficiency and data-driven decision-making makes 3D people counters an attractive proposition for businesses across diverse sectors. Competition among established players and emerging companies is driving innovation and offering various feature sets and pricing models. The forecast period (2025-2033) anticipates a sustained upward trajectory, with the market expected to be significantly larger by 2033. This growth is further underpinned by the increasing adoption of advanced analytics and the integration of 3D people counters with other business intelligence tools. The convergence of technologies, such as AI and machine learning, will further enhance the capabilities of 3D people counters, resulting in more insightful data and improved decision-making processes. Challenges remain in addressing concerns regarding data privacy and ensuring accurate counting in high-traffic environments. However, advancements in technology and robust data security measures are steadily mitigating these challenges, paving the way for widespread adoption across a broader range of industries. The market’s success depends on continued innovation, affordable pricing, and effective marketing strategies that highlight the benefits of using this technology to gain competitive advantages.

We are publishing a dataset we created for designing a brute-force detector of attacks in HTTPS. The dataset consists of extended network flows that we captured with flow exporter Ipifixprobe. Apart from traditional fields like source and destination IP addresses and ports, each flow contains information (size, direction, inter-packet time, TCP flags) about up to the first 100 packets. The sizes of packets are taken from the transport layer (TCP, UPD); packets with zero payload (e.g., TCP ACKs) are ignored. We publish three files: flows.csv, which contains raw flow data. aggregated_flows.csv, which contains aggregated flows samples.csv, which contains samples with extracted features. This data can be used for training a machine-learning classification model. All IP addresses, source ports, TLS SNIs are sha256-hashed. Column CLASS is 0 for benign samples and 1 for brute-force samples. Brute-force data The brute-force data were generated with three popular attack tools - Ncrack, Thc-hydra, and Patator. Attacks were performed against these applications: WordPress Joomla MediaWiki Ghost Grafana Discourse PhpBB OpenCart Redmine Nginx Apache The SCENARIO columns indicate which tool and application were used to generate the sample. Benign data Bening data consists of eight captures from a backbone network. The SCENARIO column indicates individual captures.

The Network Traffic Analysis (NTA) tool market is thriving, with a market size valued at XXX million in 2025 and a projected CAGR of XX% from 2025 to 2033. This growth is primarily driven by the rising concerns regarding cyber threats, the increasing adoption of cloud-based services, and the need for enhanced visibility and control over network traffic. The cloud-based segment holds a substantial market share due to its cost-effectiveness and scalability advantages. On-premises solutions, however, continue to be preferred by enterprises with stringent security requirements and data privacy concerns. Major players in the NTA tool market include Corelight, SolarWinds, Arista, Ettercap, Wireshark, Paessler, Nagios, Auvik, Icinga, Observium, ManageEngine, Elastic, NetFort, Cisco, and ExtraHop. North America dominates the market, followed by Europe and Asia Pacific. The market is anticipated to witness significant growth in emerging economies, where the adoption of NTA tools is expected to surge due to government initiatives and the increasing awareness of cyber threats. Key trends shaping the market include the integration of artificial intelligence (AI) and machine learning (ML) for automated threat detection and the convergence of NTA with other security tools, such as SIEM and EDR. The global network traffic analysis (NTA) tool market is a rapidly growing segment of the network security landscape. NTA tools provide real-time visibility into network traffic patterns, enabling organizations to identify and respond to threats and performance issues.

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%.

These are the Traffic Analysis Zones (TAZ) used in the Statewide Travel Model. This dataset contains only basic geographic information about the zones.TAZ boundaries are defined based on Census geographies (block, block group and tract). Care has been taken so that TAZ nest within Census tracts wherever possible in order for more direct matching with Census data. TAZ boundaries are also defined by major transportation facilities (such as roadways or rail lines), major environmental features (such as rivers), and with underlying land uses. The relative size of the TAZ was also a factor in deciding new TAZ boundaries if the zone size was large and the zone was thought to have a significant amount of socioeconomic activity. The size of TAZ varies from under 10 acres in the downtown to more than 100,000 acres in the mountain or lake zones. The average zone size is approximately 350 acres, which is a little over ½ square mile. Generally, TAZ in urban areas are smaller than in suburban and rural areas.There are currently 5 travel model spaces in Utah: Cache MPO (2), Dixie MPO (3), Summit (4), UDOT rural areas (0), and the combined WFRC/MAG MPO (1) model space. The model space indicators shown in parentheses above are coded in the Subarea_ID field. As travel demand model software requires that each TAZ be uniquely identified starting with the number 1, each model space has assigned its own unique TAZ identifier numbering sequence which is coded into the SubAreaTAZID field. However, this rule also applies to the statewide travel model, which is an aggregation of all the TAZs from the five model spaces into a single layer. In this statewide layer, the TAZID field is the unique identifier for the Utah Statewide Travel Model (USTM). CO_TAZID is the field used to link each TAZ to its socioeconomic data. It is a combination of the County FIPS number and a TAZ identifier within the county or from within an MPO model space.

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} }

Global Network Traffic Analyzer Market size worth at USD 2.99 Billion in 2023 and projected to USD 8.69 Billion by 2032, with a CAGR of around 12.6% between 2024-2032.