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

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

332
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
332
Hybrid Zones02:29

Hybrid Zones

17.0K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
17.0K

You might also read

Related Articles

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

Sort by
Same author

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same author

Uncovering Hierarchical Asymmetries in Artificial Intelligence Transformation: Navigating the Bright and Dark Sides Across Organizational Levels.

Journal of visualized experiments : JoVE·2026
Same author

Effects of divalent cations on diffusion dynamics of biological water confined between lipid membranes.

The Journal of chemical physics·2026
Same author

Neural Network Copulas for Generating Synthetic Test Data Preserving Psychometric Properties.

Journal of Intelligence·2026
Same author

Composite marginal likelihood estimation of higher-order diagnostic classification models under high dimensionality.

The British journal of mathematical and statistical psychology·2026
Same author

A Landmark-Guided Dual-Stream Synergistic Framework for Automated Intracranial Aneurysm Detection in Magnetic Resonance Angiography.

Journal of imaging informatics in medicine·2026

Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

A prospective approach to detect advanced persistent threats: Utilizing hybrid optimization technique.

Indra Kumari1,2, Minho Lee1,2

  • 1Department of Machine Learning Data Research, Korea Institute of Science and Technology Information (KISTI), Daejeon, 34141, Republic of Korea.

Heliyon
|November 29, 2023
PubMed
Summary

This study introduces Hybrid HHOSSA, combining Harris Hawk Optimization (HHO) and Sparrow Search Algorithm (SSA), to enhance Advanced Persistent Threat (APT) detection. The method optimizes feature selection and data balancing for improved AI-driven cybersecurity.

Keywords:
Advanced persistent threatsBi-LSTM classifiersHarris Hawk optimizationLight GBMSparrow search algorithm

More Related Videos

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.8K
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.9K

Related Experiment Videos

Last Updated: Jul 9, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.8K
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.9K

Area of Science:

  • Cybersecurity and Artificial Intelligence
  • Machine Learning Optimization Techniques

Background:

  • Advanced Persistent Threats (APTs) present complex challenges for current AI detection models.
  • Sophisticated cyber threats require novel approaches for effective mitigation.

Purpose of the Study:

  • To introduce a hybrid optimization algorithm (HHOSSA) for enhancing APT detection.
  • To optimize feature selection and data balancing for improved AI classifier performance in cybersecurity.

Main Methods:

  • Developed Hybrid HHOSSA by integrating Harris Hawk Optimization (HHO) and Sparrow Search Algorithm (SSA).
  • Applied HHOSSA for attribute selection and data balancing (HHOSSA-SMOTE) on the DAPT 2020 dataset.
  • Optimized LightGBM and weighted average Bi-LSTM classifiers using HHOSSA for hyperparameter tuning.

Main Results:

  • Achieved 94.468% accuracy, 94.650% sensitivity, and 95.230% specificity with 10-fold cross-validation.
  • The HHOSSA-hybrid classifier demonstrated a high Area Under the Curve (AUC) of 97.032%.
  • Significant improvements in detecting lateral movements and data exfiltration were observed.

Conclusions:

  • The HHOSSA-hybrid approach significantly enhances the accuracy and effectiveness of APT attack detection.
  • Optimized feature selection and data balancing are crucial for robust AI-based cybersecurity solutions.
  • The proposed method offers a promising advancement in combating sophisticated cyber threats.