This set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=0.2. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.

Test Traffic Sign

## Overview

Test Traffic Sign is a dataset for object detection tasks - it contains Stop And Right annotations for 401 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).

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.

ASNM datasets include records consisting of many features

Only Test Site Korean Traffic Light 2

## Overview

Only Test Site Korean Traffic Light 2 is a dataset for object detection tasks - it contains Green Red Left PZAm annotations for 2,038 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).

CaualRL/traffic-video-test dataset hosted on Hugging Face and contributed by the HF Datasets community

NetFlow traffic generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic) NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.

NetFlow flows have been captured with sampling 250 at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued.

The version of NetFlow used to build the datasets is 5.

Network Traffic Analyzer Market Outlook

In 2023, the global network traffic analyzer market size was valued at approximately USD 2.5 billion and is anticipated to grow to USD 6.7 billion by 2032, with a CAGR of 11.5% during the forecast period. The significant growth factor driving this market is the increasing demand for sophisticated network management tools to manage the exponential growth in data traffic. As enterprises continue to digitize their operations, the necessity for advanced network traffic analysis solutions escalates, ensuring network reliability, security, and performance.

The growth of the network traffic analyzer market is propelled by several key factors. Firstly, the rapid expansion of internet usage and the proliferation of connected devices generate vast amounts of data traffic, necessitating robust tools to monitor and analyze this traffic effectively. With the surge in cyber threats and network security breaches, organizations are increasingly adopting network traffic analyzers to detect, respond to, and mitigate potential security risks. The ability of these tools to provide real-time visibility into network operations and detect anomalies is critical in safeguarding enterprise networks.

Secondly, the advent of advanced technologies such as Internet of Things (IoT), cloud computing, and 5G networks has significantly boosted the demand for network traffic analyzers. These technologies generate enormous amounts of network traffic, which need to be monitored and managed efficiently to ensure optimal performance and security. Network traffic analyzers play a vital role in managing this complexity, offering insights that help in optimizing network resources and improving overall operational efficiency.

Another major growth factor is the increasing adoption of network traffic analyzer solutions by small and medium enterprises (SMEs). Traditionally, these solutions were predominantly used by large enterprises due to their high cost and complexity. However, recent advancements have made these tools more accessible and affordable for SMEs, enabling them to harness the benefits of network traffic analysis. This democratization of technology is expected to further drive market growth in the coming years.

From a regional perspective, North America currently holds the largest share of the network traffic analyzer market, driven by strong technological infrastructure and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation in countries like China and India, coupled with increasing investments in network infrastructure and cybersecurity, is propelling the market forward in this region. Europe and Latin America are also expected to see steady growth, driven by regulatory mandates and the increasing need for network security solutions.

Component Analysis

The network traffic analyzer market is segmented by component into software, hardware, and services. The software segment holds the largest market share, attributed to the increasing deployment of advanced software solutions for network monitoring and traffic analysis. These software solutions offer comprehensive insights into network performance, enabling organizations to proactively manage network issues and optimize performance. With the integration of AI and machine learning, these solutions are becoming even more sophisticated, capable of predictive analysis and automated responses.

Network Packet Broker (NPB) solutions are becoming increasingly vital in the context of network traffic analysis. These devices play a crucial role in optimizing the flow of data across complex network infrastructures by aggregating, filtering, and directing traffic to specific monitoring tools. As networks grow in complexity with the proliferation of IoT devices and cloud services, NPBs help ensure that only relevant data is sent to analysis tools, thereby enhancing the efficiency and accuracy of network monitoring. By offloading the processing burden from network analyzers, NPBs enable organizations to maintain high performance and reliability in their network operations, making them an indispensable component of modern network management strategies.

The hardware segment, although smaller than the software segment, plays a crucial role in the network traffic analyzer market. Hardware components such as network probes, packet brokers, an

This research is based on data gathered in 2020 in the Netherlands.

A controlled field test was conducted in which human drivers interacted with both human-driven vehicles (HDVs) and automated vehicles (AVs). Participants were asked to drive in their own vehicle (because of COVID-19 restrictions). During the field test, the participants interacted with an instrumented test vehicle that could be set up to appear as an AV. The instrumented test vehicle collected data on the driving behavior of the participant during their interactions. The field test was approved by the Human Research Ethics Committee of the Delft University of Technology, the Netherlands.

The field test was conducted on a 3 km long straight road section in Noordzeeweg near the town of Rozenburg in the Netherlands. The test route had one 3.5-m wide lane per direction separated by dashed lane markings (i.e., overtaking was allowed). The traffic intensity of the test location was very low (around 30 vehicles per hour), and the speed limit of the road section is 60 km/h. A depiction of the route is attached in this dataset information.

18 participants took part in the field test.14 participants were between 35 and 60 years old, and 4 participants were younger than 35 years old. Behaviors of gap acceptance, overtaking, and car-following were studied, while the scenarios varied with the appearance of the test vehicle, either as an HDV or AV.

More details about the experiment set-up itself can be found in our paper published: https://doi.org/10.1016/j.trf.2022.02.002

Attached files:

Processed dataset

ReadMe file

File formats:

Data (created and used in matlab) /.sav

The global synthetic traffic generator market size was valued at USD 832 million in 2025 and is projected to reach USD 1,333 million by 2033, exhibiting a CAGR of 8.2% during the forecast period. The growth of the market is attributed to the increasing demand for network performance testing and system evaluation, network security, and the need for efficient and cost-effective testing solutions. The market is expected to be driven by the adoption of software-defined networking (SDN) and network function virtualization (NFV), which enable the creation of dynamic and scalable networks. Additionally, the growing awareness of the importance of network security and the need for comprehensive testing to identify and mitigate vulnerabilities is expected to further contribute to the market growth. The key players in the synthetic traffic generator market include Keysight Technologies, BittWare, SolarWinds Worldwide, LLC, NagleCode, LLC, Apposite Technologies, East Coast Datacom Inc, ostinato.org, EasyTrafficBot UG, SparkTraffic, Northwest Performance Software, Inc., among others. These players are focusing on developing innovative solutions to meet the evolving needs of their customers and to maintain their competitive advantage. Partnerships, acquisitions, and new product launches are some of the key strategies adopted by these players to expand their market presence. The market is also witnessing the emergence of new startups and small businesses, which are offering innovative and cost-effective solutions to cater to the diverse needs of customers.

The US Network Traffic Analyzer is projected to be valued at $2.5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 10.5%, reaching approximately $6.4 billion by 2034.

This dataset is designed for traffic surveillance anomaly detection, originally from the WSAL (Weakly-Supervised Anomaly Localization) repository. It consists of 500 short video clips totaling approximately 25 hours of footage. Each clip averages around 1,075 frames, and anomalies, when present, typically span around 80 frames.

• Number of videos: 500
  • Abnormal videos: 250
  • Normal videos: 250
• Average duration (frames) per clip: ~1,075
• Average anomaly length (frames): ~80
• Total duration: ~25 hours
• Partition:
  • Training set: 400 videos
  • Test set: 100 videos

Each video is labeled to indicate whether it contains an anomaly or not, enabling both supervised training and evaluation. You can use the labels to develop or compare different anomaly detection methods.

Citation

If you use this dataset for your research, please cite the following paper:

@article{wsal_tip21,
 author  = {Hui Lv and
        Chuanwei Zhou and
        Zhen Cui and
        Chunyan Xu and
        Yong Li and
        Jian Yang},
 title   = {Localizing Anomalies from Weakly-Labeled Videos},
 journal  = {IEEE Transactions on Image Processing (TIP)},
 year   = {2021}
}

For more details about how the dataset was created and used, see the original WSAL GitHub repository.

This data set was acquired by the USDOT Data Capture and Management program. The purpose of the data set is to provide multi-modal data and contextual information (weather and incidents) that can be used to research and develop applications. Contains one full year (January – December 2010) of raw 30-second data for over 3,000 traffic detectors deployed along 1,250 lane miles of monitored roadway in San Diego. Cleaned and geographically referenced data for over 1,500 incidents and lane closures for the two sections of I-5 that experienced the greatest number of incidents during 2010. Complete trip (origin-to-destination) GPS “breadcrumbs” collected by ALK Techonologies, containing latitude/longitude, vehicle heading and speed data, and time for individual in-vehicles devices updated at 3-second intervals for over 10,000 trips taken during 2010. A digital map shape file containing ALK’s street-level network data for the San Diego Metropolitan area. And San Diego Weather data for 2010. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

Netflow traffic generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic) NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device. Netflow flows have been captured by sampling at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued. In the construction of the datasets, different percentages of flows considered attacks and flows considered normal traffic have been used. These datasets have been used to test previously trained models.

The network traffic analyzer is projected to be valued at $1.5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $4.6 billion by 2034.

Global Network Traffic Analyzer Market is poised to witness substantial growth, reaching a value of USD 9.49 Billion by the year 2033, up from USD 3.79 Billion attained in 2024. The market is anticipated to display a Compound Annual Growth Rate (CAGR) of 10.73% between 2025 and 2033.

The Global Network Traffic Analyzer market size to cross USD 9.49 Billion by 2033. [https://edison.valuemarketrese

Network Traffic Analyzer Market size was valued at USD 3.54 Billion in 2024 and is projected to reach USD 5.86 Billion by 2032, growing at a CAGR of 10.6% during the forecast period 2026-2032.

Global Network Traffic Analyzer Market Drivers

The market drivers for the Network Traffic Analyzer Market can be influenced by various factors. These may include:

Growing Risks to Cybersecurity: The increasing sophistication and frequency of cyber threats and attacks are driving the need for network traffic analyzers to improve security protocols. These instruments support the identification and mitigation of dubious network activity. Increasing Network Infrastructure Complexity: Organisations need sophisticated tools to monitor and analyze network traffic because network infrastructures, especially hybrid and multi-cloud systems, are becoming more and more complicated. Network traffic analyzers shed light on these complex infrastructures' security and performance. Growing Cloud Computing Adoption: There is a growing need for network traffic analyzers that can monitor and optimize performance across cloud environments due to the widespread adoption of cloud services and the migration of applications and data to the cloud.

Traffic analytics, rankings, and competitive metrics for testing-library.com as of May 2025

Here is the dataset for classifying the different classes of traffic signs. There are around 58 classes and each class has around 120 images. the labels.csv file has the respective description of the traffic sign class. You can change the assignment of these classIDs with descriptions. We can use the basic CNN model to get decent val accuracy. We have around 2000 files for testing.

You can view the notebook named official in the code section to train and test basic cnn model.

Please upvote the notebook and dataset if you like this.

The Network Traffic Analyzer Market size is expected to reach a valuation of USD xx billion in 2033 growing at a CAGR of xx%. The Network Traffic Analyzer Market research report classifies Market by share, trend, demand, forecast and based on segmentation.