Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

17.8K
The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
17.8K
Signal Flow Graphs01:18

Signal Flow Graphs

787
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
787
Flow Table Test01:12

Flow Table Test

1.0K
The flow table test is an established method used to assess the workability of concrete, particularly useful for evaluating highly flowable concrete mixes. This test employs an apparatus that consists of a wooden board topped with a steel plate, collectively weighing 35 pounds. The board is connected to a base via a hinge and measures 27.6 inches on each side.
Concrete is placed within a truncated cone mold that is 8 inches high with an 8-inch base diameter and a 5-inch top diameter. The...
1.0K
Introduction to Types of Flows01:23

Introduction to Types of Flows

2.1K
Fluid flows are categorized by dimensionality and behavior, with one-dimensional flow being the simplest form, where properties like velocity and pressure change only along a single axis. Water moving through straight pipes exemplifies this flow type, as variations in other directions are minimal. One-dimensional analysis helps simplify understanding such flows, focusing solely on changes along the pipe's length.
Two-dimensional flow involves changes in both length and height, as seen in...
2.1K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

823
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant...
823
Rapidly Varying Flow01:24

Rapidly Varying Flow

707
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
707

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An innovative method for assessing the relationship between longitudinal brain volume measurements and neurodevelopmental outcomes in preterm infants.

Computers in biology and medicine·2025
Same author

A real-world iiot dataset for predictive maintenance of metalworking fluids.

Data in brief·2025
Same author

SEM-EDS and hyperspectral images of vine leaves treated with antifungal products.

Data in brief·2025
Same author

Dataset for defect detection in textile manufacturing.

Data in brief·2025
Same author

Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.

Frontiers in artificial intelligence·2025
Same author

Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral.

Sensors (Basel, Switzerland)·2023
Same journal

A harmonized fast-fashion garment-variant dataset for textile circularity and sustainability assessment.

Data in brief·2026
Same journal

Terahertz reflectivity dataset: Reading text on both sides of the page.

Data in brief·2026
Same journal

High-quality draft genome sequence data of <i>Levilactobacillus brevis</i> 3LB isolated from fermented milk koumiss.

Data in brief·2026
Same journal

Interview dataset: Encouraging the development of industrial symbiosis networks in Slovenia - transition to the circular economy.

Data in brief·2026
Same journal

Timeseries of multispectral and radar data and vegetation indices from Sentinel-1, Sentinel-2 and Landsat-8 at field scale.

Data in brief·2026
Same journal

BACI-VI-Bench: A dataset of variational inequality benchmark instances for multi-agent trade-network equilibrium.

Data in brief·2026
See all related articles

Related Experiment Video

Updated: Apr 26, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

9.7K

Labelled IoT flow-based network traffic dataset for cyberattack detection.

Branly Martínez1, Carlos Cambra1, Daniel Urda1

  • 1Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, Burgos 09006, Spain.

Data in Brief
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a labeled dataset of Internet of Things (IoT) network traffic, capturing both normal operations and cyberattacks. This resource aids in developing advanced IoT cybersecurity defenses.

Keywords:
Attack trafficBenign trafficCybersecurityFlow featuresGround truthInternet of thingsLabellingNetwork

Related Experiment Videos

Last Updated: Apr 26, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

9.7K

Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • The Internet of Things (IoT) ecosystem faces increasing cybersecurity threats.
  • A lack of comprehensive, labeled datasets hinders the development of effective IoT security solutions.
  • Existing datasets may not adequately represent diverse attack vectors or benign traffic patterns.

Purpose of the Study:

  • To present a novel, labeled flow-based network traffic dataset from a controlled IoT laboratory environment.
  • To provide a valuable resource for research in IoT traffic analysis and cyberattack detection.
  • To facilitate the development and validation of machine learning models for IoT security.

Main Methods:

  • Collected network traffic at the packet level using passive monitoring in a Raspberry Pi-based IoT testbed.
  • Processed packet captures into bidirectional network flows with statistical and temporal attributes.
  • Labeled network flows as benign or malicious based on experimental ground-truth and attack scenario markers.
  • Included raw packet captures, labeled flow files, and supporting metadata in the dataset release.

Main Results:

  • A comprehensive dataset containing labeled network flows from normal operations and nine distinct cyberattack scenarios across six categories.
  • The dataset distinguishes between benign and attack flows using a combined ground-truth and IP address labeling approach.
  • Raw packet captures (PCAPNG) and processed flow records are provided for detailed analysis.

Conclusions:

  • The released dataset offers a robust foundation for advancing IoT cybersecurity research.
  • It enables researchers to develop and test novel cyberattack detection algorithms.
  • The structured repository supports reproducible research and potential re-labeling for further exploration.