The report covers Global Network Traffic Analysis Market Share and it is segmented by Deployment (On-premise and Cloud-based), End-user Vertical (BFSI, IT and Telecom, Government, Energy and Power, and Retail), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The market sizes and forecasts are provided in value (in USD million) for all the above segments.

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

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

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

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

Traffic Analysis Zones (TAZ) for the COG/TPB Modeled Region from Metropolitan Washington Council of Governments. The TAZ dataset is used to join several types of zone-based transportation modeling data. For more information, visit https://plandc.dc.gov/page/traffic-analysis-zone.

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

Unlock the Potential of Your Web Traffic with Advanced Data Resolution

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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.

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.

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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...

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 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current Traffic Analysis Zones for Torrance County stored in the 2006 TIGER Second Edition dataset.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current Traffic Analysis Zones for Bernalillo County stored in the 2006 TIGER Second Edition dataset.

Traffic Analysis UTB

## Overview

Traffic Analysis UTB is a dataset for object detection tasks - it contains Car Truck Bus Pedestrian Van annotations for 6,462 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 [MIT license](https://creativecommons.org/licenses/MIT).

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.

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.

More Metadata

This layer contains the geographical boundaries of the Metropolitan Washington Council of Government's Traffic Analysis Zones (TAZ) of Loudoun County, Virginia. TAZs are designed to be relatively homogeneous units with respect to population, economic, and transportation characteristics. These TAZ boundaries were delineated by Loudoun County Government and adopted by the Metropolitan Washington Council of Governments.

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

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

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current Traffic Analysis Zones for Sandoval County stored in the 2006 TIGER Second Edition dataset.

The Network Traffic Analysis Solutions Market is projected to grow at 13.7% CAGR, reaching $10.07 Billion by 2029. Where is the industry heading next? Get the sample report now!

Traffic Analysis Zones

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:

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

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Fredericksburg VA Public data for download.