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

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

You might also read

Related Articles

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

Sort by

Same author

Isolation of the Stromal Vascular Fraction Using a New Protocol with All Clinical-Grade Drugs: From Basic Study to Clinical Application.

Aesthetic plastic surgery·2024

Same author

Autologous Tissue Repair and Total Face Restoration.

JAMA otolaryngology-- head & neck surgery·2024

Same author

Predicting anion diffusion in bentonite using hybrid machine learning model and correlation of physical quantities.

The Science of the total environment·2024

Same author

Epidemiologic and survival analysis of malignant peripheral nerve sheath tumors: a retrospective cohort study.

International journal of surgery (London, England)·2024

Same author

Genotype-phenotype correlations and treatment innovation of neurofibromatosis type 1 (NF1): Current understanding and perspective.

Chinese medical journal·2024

Same author

Estimating helmet wearing rates via a scalable, low-cost algorithm: a novel integration of deep learning and google street view.

BMC public health·2024

Search research articles

Related Experiment Video

Updated: May 17, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Malicious traffic prediction model for ResNet based on Maple-IDS dataset.

Qingfeng Li¹, Boyu Wang², Xueyan Wen²

¹Network Information Center, Northeast Forestry University, Heilongjiang, China.

|May 13, 2025

Summary

This summary is machine-generated.

This study introduces the Maple-IDS dataset to improve cyberattack detection accuracy. By balancing attack data and using a novel prediction model, it achieves 99.83% accuracy in identifying malicious network traffic.

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Related Experiment Videos

Last Updated: May 17, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Area of Science:

Cybersecurity
Network Intrusion Detection
Machine Learning

Background:

Cyberattacks pose a significant threat, necessitating accurate malicious network traffic identification.
Imbalanced attack data in existing datasets reduces the accuracy of intrusion detection models.
Existing network security prediction models suffer from low accuracy and slow convergence.

Purpose of the Study:

To introduce the Maple-IDS dataset for more balanced attack data representation.
To develop an improved network situation awareness prediction model.
To enhance the accuracy and speed of detecting network security threats.

Main Methods:

Developed the Maple-IDS dataset using DPDK, zero-copy (ZC) technology, and BPF compiler.
Employed a headless client to generate control traffic and prevent overfitting.
Integrated a residual network with an attention mechanism for anomaly detection and faster convergence.

Main Results:

The Maple-IDS dataset provides a more balanced representation of attack data compared to CIC-IDS-2017.
The proposed model achieved a 99.83% accuracy in predicting attack data flows.
The integrated model demonstrated accelerated convergence speed and enhanced expressive capability.

Conclusions:

The Maple-IDS dataset and the novel prediction model significantly improve network intrusion detection.
Accurate and rapid identification of network threats enables preemptive measures for normal network operations.
The developed approach enhances the efficiency of responding to cyberattacks.