Overview

This record provides a dataset created as part of the study presented in the following publication and is made publicly available for research purposes. The associated article provides a comprehensive description of the dataset, its structure, and the methodology used in its creation. If you use this dataset, please cite the following article published in the journal IEEE Communications Magazine:

A. Karamchandani, J. Nunez, L. de-la-Cal, Y. Moreno, A. Mozo, and A. Pastor, “On the Applicability of Network Digital Twins in Generating Synthetic Data for Heavy Hitter Discrimination,” IEEE Communications Magazine, pp. 2–8, 2025, DOI: 10.1109/MCOM.003.2400648.

More specifically, the record contains several synthetic datasets generated to differentiate between benign and malicious heavy hitter flows within a realistic virtualized network environment. Heavy Hitter flows, which include high-volume data transfers, can significantly impact network performance, leading to congestion and degraded quality of service. Distinguishing legitimate heavy hitter activity from malicious Distributed Denial-of-Service traffic is critical for network management and security, yet existing datasets lack the granularity needed for training machine learning models to effectively make this distinction.

To address this, a Network Digital Twin (NDT) approach was utilized to emulate realistic network conditions and traffic patterns, enabling automated generation of labeled data for both benign and malicious HH flows alongside regular traffic.

Feature Set:

The feature set includes the following flow statistics commonly used in the literature on network traffic classification:

• The protocol used for the connection, identifying whether it is TCP, UDP, ICMP, or OSPF.
• The time (relative to the connection start) of the most recent packet sent from source to destination at the time of each snapshot.
• The time (relative to the connection start) of the most recent packet sent from destination to source at the time of each snapshot.
• The cumulative count of data packets sent from source to destination at the time of each snapshot.
• The cumulative count of data packets sent from destination to source at the time of each snapshot.
• The cumulative bytes sent from source to destination at the time of each snapshot.
• The cumulative bytes sent from destination to source at the time of each snapshot.
• The time difference between the first packet sent from source to destination and the first packet sent from destination to source.

Dataset Variations:

To accommodate diverse research needs and scenarios, the dataset is provided in the following variations:

1. All at Once:

   1. Contains a synthetic dataset where all traffic types, including benign, normal, and malicious DDoS heavy hitter (HH) flows, are combined into a single dataset.
   2. This version represents a holistic view of the traffic environment, simulating real-world scenarios where all traffic occurs simultaneously.

2. Balanced Traffic Generation:

   1. Represents a balanced traffic dataset with an equal proportion of benign, normal, and malicious DDoS traffic.
   2. Designed for scenarios where a balanced dataset is needed for fair training and evaluation of machine learning models.

3. DDoS at Intervals:

   1. Contains traffic data where malicious DDoS HH traffic occurs at specific time intervals, mimicking real-world attack patterns.
   2. Useful for studying the impact and detection of intermittent malicious activities.

4. Only Benign HH Traffic:

   1. Includes only benign HH traffic flows.
   2. Suitable for training and evaluating models to identify and differentiate benign heavy hitter traffic patterns.

5. Only DDoS Traffic:

   1. Contains only malicious DDoS HH traffic.
   2. Helps in isolating and analyzing attack characteristics for targeted threat detection.

6. Only Normal Traffic:

   1. Comprises only regular, non-HH traffic flows.
   2. Useful for understanding baseline network behavior in the absence of heavy hitters.

7. Unbalanced Traffic Generation:

   1. Features an unbalanced dataset with varying proportions of benign, normal, and malicious traffic.
   2. Simulates real-world scenarios where certain types of traffic dominate, providing insights into model performance in unbalanced conditions.

For each variation, the output of the different packet aggregators is provided separated in its respective folder.

Each variation was generated using the NDT approach to demonstrate its flexibility and ensure the reproducibility of our study's experiments, while also contributing to future research on network traffic patterns and the detection and classification of heavy hitter traffic flows. The dataset is designed to support research in network security, machine learning model development, and applications of digital twin technology.

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Time-Sensitive Networking Market Size 2024-2028

The time-sensitive networking market size is forecast to increase by USD 2.84 billion, at a CAGR of 51.3% between 2023 and 2028.

The Time-Sensitive Networking (TSN) market is experiencing significant growth, driven by the rapid digitization of various industries. The increasing adoption of Industry 4.0 and the Internet of Things (IoT) is leading to an escalating demand for real-time data transfer and processing capabilities. This trend is further fueled by new product launches from companies, offering advanced features and functionalities that cater to the specific needs of time-critical applications. However, the market faces challenges that require careful consideration. Security concerns are a major obstacle, as the implementation of TSN networks exposes organizations to potential cyber threats. Ensuring robust security measures is essential to mitigate risks and maintain data integrity.
Additionally, the complexity of TSN technology and the need for interoperability between different systems can pose implementation challenges for organizations. Addressing these challenges through strategic partnerships, collaborations, and continuous innovation will be crucial for market participants seeking to capitalize on the opportunities presented by the TSN market.

What will be the Size of the Time-Sensitive Networking Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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Time-sensitive networking (TSN) continues to gain momentum in various sectors, including medical devices, process control, and remote surgery, as the need for real-time, low latency communication becomes increasingly crucial. Wireless communication and autonomous vehicles also benefit from TSN's capabilities, enabling seamless integration of network deployment and network topologies. Network scheduling and network switches play a pivotal role in ensuring data reliability and integrity, while network monitoring and network topologies facilitate network management and optimization. TSN's deterministic networking properties are essential for data acquisition in applications such as smart grid and industrial automation. Network routers and network interfaces are crucial components in TSN architectures, ensuring standard compliance and efficient bandwidth allocation.

Real-time Ethernet and IEEE 802.1QBU protocols provide the foundation for high-speed data transmission, enabling real-time data processing and network performance enhancement. Network security remains a priority, with TSN's network protocols and traffic management features ensuring data transmission's integrity and preventing packet loss. IEEE 802.1AS and IEEE 802.1QBV standards further enhance TSN's capabilities, enabling edge computing and network configuration flexibility. Cloud computing and industrial ethernet are among the many applications of TSN, as the demand for low latency communication and deterministic networking grows across industries. The ongoing evolution of TSN continues to unfold, with new applications and innovations emerging in the realm of network management, data transmission, and network optimization.

How is this Time-Sensitive Networking Industry segmented?

The time-sensitive networking industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Hardware
  Software
  Services


End-user

  Industrial automation
  Automotive
  Digital communication
  Power and energy
  Others


Geography

  North America

    US
    Canada


  Europe

    Germany


  APAC

    China


  Rest of World (ROW)

.

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period.

The time-sensitive networking (TSN) market is witnessing significant growth, driven by the increasing demand for synchronized, low-latency communication across various industries. The hardware segment plays a crucial role in this market, providing the necessary infrastructure through advanced Ethernet switches, routers, and other networking devices. These TSN-enabled hardware solutions are essential for applications in industrial automation, intelligent transportation, smart power grids, and more. Recent innovations in TSN hardware include Fiberroad's new TSN Industrial Ethernet Switch, which features TSN technology and IEEE 1588 Precision Time Protocol. This switch enhances real-time communication and network reliability, contributing to the growth and adoption of TSN technology in markets such as automotive, energy, and others.

TSN technology is also crucial in applications like medical devices,

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Ultra Wideband Market Size 2024-2028

The ultra wideband market size is forecast to increase by USD 702.8 million, at a CAGR of 9.9% between 2023 and 2028.

The Ultra Wideband (UWB) market is experiencing significant growth due to the increasing adoption of Real-Time Location System (RTLS) technology in various industries. UWB's ability to provide precise location information in real-time makes it an attractive solution for asset tracking, indoor navigation, and proximity marketing applications. However, this market is not without challenges. Cybersecurity vulnerabilities pose a significant threat, as UWB's long-range and low-power capabilities make it susceptible to potential hacking and data breaches. Companies must prioritize implementing robust security measures to mitigate these risks and protect sensitive information.
Despite these challenges, the opportunities for innovation and growth in the UWB market are substantial, particularly in sectors such as healthcare, logistics, and retail. By focusing on developing secure and reliable UWB solutions, businesses can capitalize on the technology's potential to enhance operational efficiency and deliver new value-added services to their customers.

What will be the Size of the Ultra Wideband Market during the forecast period?

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Ultra-wideband (UWB) technology continues to evolve, offering versatile applications across various sectors. With its ability to provide high precision positioning and low power consumption, UWB is increasingly adopted in industries such as robotics, healthcare, and automotive. UWB's large bandwidth enables high data rate transmission, making it an ideal choice for various applications. UWB's channel estimation and ranging capabilities ensure accurate and reliable communication, even in complex environments. The ongoing development of UWB standards, such as IEEE 802.15.4a and Fira Consortium's 802.15.4z, further enhances its capabilities and interoperability. UWB's integration in wearables and sensors offers new possibilities for real-time monitoring and tracking.

However, challenges such as multipath propagation, privacy concerns, and interference mitigation require continuous attention from industry players. UWB's precision and low latency make it a promising technology for time-of-flight applications. Its potential in healthcare, for instance, includes non-invasive medical imaging and patient monitoring. In automotive, UWB is being explored for vehicle-to-vehicle communication and collision avoidance systems. UWB's evolving nature calls for ongoing efforts in power consumption optimization, bandwidth expansion, and security enhancements. The technology's potential is vast, and its continuous unfolding promises new applications and innovations.

How is this Ultra Wideband Industry segmented?

The ultra wideband industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Communication
  RTLS
  Imaging


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The communication segment is estimated to witness significant growth during the forecast period.

Ultra Wideband (UWB) technology is an advanced communication method characterized by its wide bandwidth and low power consumption. UWB modules and chips, essential components of UWB systems, enable high-precision positioning and low-latency data transfer. UWB transceivers facilitate reliable communication between devices, while location tracking and multipath propagation enhance accuracy. UWB's privacy features ensure secure data transmission, and standards, such as IEEE 802.15.4a and Fira Consortium's UWB, ensure interoperability. UWB's applications extend to robotics, wearables, healthcare, and automotive industries. In robotics, UWB enables precise control and positioning. In wearables, it powers continuous health monitoring. In healthcare, UWB's accuracy and low power consumption make it suitable for medical devices.

In automotive, UWB's time-of-flight technology enhances safety and parking systems. UWB sensors and positioning systems offer real-time data, while compliance with regulations ensures safety and reliability. UWB's low power consumption and high bandwidth make it an attractive alternative to Bluetooth. UWB's precision and interference mitigation capabilities improve performance and reliability. UWB's channel estimation and ranging features enable accurate distance measurement. UWB antennas optimize signal transmission and reception. UWB's security features protect data from unauthorized access, making it a preferred choice for secure com