Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Flow Cytometry

Flow Cytometry

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...

Flow Table Test

Flow Table Test

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...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Introduction to Types of Flows

Introduction to Types of Flows

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...

You might also read

Related Articles

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

Sort by

Same author

Use of animal-mounted accelerometers to identify positive welfare in dairy cattle.

Dairy science and management·2025

Same author

A Novel Online Position Estimation Method and Movement Sonification System: The Soniccup.

Sensors (Basel, Switzerland)·2024

Same author

Multi-Modal Convolutional Parameterisation Network for Guided Image Inverse Problems.

Journal of imaging·2024

Same author

Sleep quality and duration: A key to life satisfaction among military students.

Military psychology : the official journal of the Division of Military Psychology, American Psychological Association·2023

Same author

Feed Conversion Ratio (FCR) and Performance Group Estimation Based on Predicted Feed Intake for the Optimisation of Beef Production.

Sensors (Basel, Switzerland)·2023

Same author

A Sensor Network Utilizing Consumer Wearables for Telerehabilitation of Post-Acute COVID-19 Patients.

IEEE internet of things journal·2022

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Published on: September 29, 2023

Utilising Flow Aggregation to Classify Benign Imitating Attacks.

Hanan Hindy¹, Robert Atkinson², Christos Tachtatzis²

¹Division of Cybersecurity, Abertay University, Dundee DD1 1HG, UK.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

This summary is machine-generated.

New network traffic features improve cyber-attack detection by aggregating similar flows. This method enhances machine learning models to identify sophisticated attacks that mimic benign behavior, boosting overall accuracy.

Keywords:

CICIDS2017 NetFlow cyber-attacks features intrusion detection machine learning network traffic

More Related Videos

Flow Virometry to Analyze Antigenic Spectra of Virions and Extracellular Vesicles

Flow Virometry to Analyze Antigenic Spectra of Virions and Extracellular Vesicles

Published on: January 25, 2017

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Related Experiment Videos

Last Updated: Nov 10, 2025

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Published on: September 29, 2023

Flow Virometry to Analyze Antigenic Spectra of Virions and Extracellular Vesicles

Flow Virometry to Analyze Antigenic Spectra of Virions and Extracellular Vesicles

Published on: January 25, 2017

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Area of Science:

Computer Science
Cybersecurity
Network Security

Background:

Cyber-attacks are increasing in volume and sophistication, posing significant threats.
Existing machine learning defenses struggle with attacks that imitate benign network traffic.
Feature engineering is crucial for effective cyber-attack detection models.

Purpose of the Study:

To introduce novel features for enhanced cyber-attack detection.
To improve the classification of cyber-attacks that mimic benign behavior.
To advance feature extraction techniques for complex cyber threats.

Main Methods:

Developed new features through higher-level abstraction of network traffic.
Implemented flow aggregation by grouping similar network flows.
Evaluated feature performance using the CICIDS2017 dataset.

Main Results:

The proposed features demonstrated validity and effectiveness in cyber-attack detection.
The new features improved the ability to classify attacks mimicking benign traffic.
Enhanced detection accuracy was achieved for complex cyber-attacks.

Conclusions:

Flow aggregation offers a promising direction for cyber-attack feature extraction.
The novel features significantly improve the accuracy of cyber-attack detection systems.
This approach contributes to building more robust defenses against evolving cyber threats.