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

Search research articles

Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Detection of Algorithmically Generated Domain Names Using the Recurrent Convolutional Neural Network with Spatial

Zhanghui Liu^1,2, Yudong Zhang¹, Yuzhong Chen^1,2

¹Fujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Bibliometric analysis of research trends on nanotechnology applications in atherosclerosis.

Discover nano·2026

Same author

Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.

Journal of thoracic disease·2026

Same author

Post-liver transplantation delirium: Pathogenesis, risk factors, clinical management, and future directions.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society·2026

Same author

Spin-Polarized Luminescence Modulated by Magnetic Coupling in Glass-Embedded Eu<sup>2+</sup>-Doped Lead-Free Perovskite Nanocrystals.

ACS nano·2026

Same author

Ecosystem simulation: the software to platform leap.

Scientific reports·2026

Same author

Hydrogen crossover raises serious concerns on proton exchange membrane water electrolyzer.

Innovation (Cambridge (Mass.))·2026

Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026

Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026

Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026

Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026

Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026

Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026

See all related articles

This summary is machine-generated.

This study introduces a new deep learning model, the Recurrent Convolutional Neural Network with Spatial Pyramid Pooling (RCNN-SPP), to accurately detect malicious domain generation algorithms (DGAs). The RCNN-SPP model significantly improves detection accuracy and robustness against botnets and malware.

Area of Science:

Cybersecurity
Machine Learning
Network Security

Background:

Domain Generation Algorithms (DGAs) create numerous random domain names, complicating the detection of botnets and malware.
Traditional detection methods rely on manual feature engineering, which is labor-intensive and often insufficient.
Existing deep learning models struggle with imbalanced datasets and fail to fully leverage discriminative features for accurate DGA detection.

Purpose of the Study:

To enhance the detection accuracy and robustness of identifying algorithmically generated domain names.
To address the challenges of imbalanced sample distribution and feature extraction in DGA detection models.
To propose an improved deep learning framework for distinguishing between DGA and legitimate domain names.

Main Methods:

Keywords:

SMOTE algorithmically generated domain name domain generation algorithm recurrent convolutional neural network spatial pyramid pooling

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Related Experiment Videos

Last Updated: Nov 27, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Employed the borderline synthetic minority over-sampling algorithm (SMOTE) to balance the dataset distribution.
Developed a Recurrent Convolutional Neural Network with Spatial Pyramid Pooling (RCNN-SPP) for feature extraction.
The RCNN-SPP model integrates Convolutional Neural Networks (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) networks, enhanced with spatial pyramid pooling for multi-scale contextual information.

Main Results:

Achieved 92.36% accuracy, 89.55% recall, 90.46% F1-score, and 95.39% AUC in binary classification of DGA and legitimate domains.
Demonstrated 92.45% accuracy, 90.12% recall, 90.86% F1-score, and 96.59% AUC in multi-classification tasks.
Showcased significant improvements in accuracy and robustness compared to existing DGA detection models.

Conclusions:

The proposed RCNN-SPP model effectively addresses sample imbalance and extracts discriminative features for superior DGA detection.
The integration of CNN, Bi-LSTM, and spatial pyramid pooling enhances the model's ability to capture semantic and contextual information.
The RCNN-SPP model offers a robust and accurate solution for identifying malicious domain names, improving cybersecurity defenses against botnets and malware.