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

Aggregates Classification01:29

Aggregates Classification

370
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...
370
Force Classification01:22

Force Classification

1.5K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.5K
Classification of Systems-II01:31

Classification of Systems-II

224
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
224
Classification of Leukocytes01:30

Classification of Leukocytes

2.4K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.4K
Classification of Systems-I01:26

Classification of Systems-I

289
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
289
Methods of Classification and Identification01:28

Methods of Classification and Identification

156
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
156

You might also read

Related Articles

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

Sort by
Same author

Maceration in White Winemaking: Enhancing Phenolics, Volatile Aromas and Sensory Characteristics of Chardonnay and Italian Riesling Wines.

Molecules (Basel, Switzerland)·2026
Same author

Core gastric microbiota linked to pathogenesis and preserved across age-stratified cohorts.

Cell reports. Medicine·2026
Same author

hsa-miR-375-3p in embryo culture medium exosomes as a preimplantation non-invasive biomarker for predicting live birth after IVF.

Reproductive biomedicine online·2026
Same author

Assessing the health utility values of patients with six common cancers in China using the QLU-C10D: A cross-sectional survey.

iScience·2026
Same author

Severe anaphylactic shock reaction upon rituximab rechallenge in membranous nephropathy: a case report and literature review.

Frontiers in pharmacology·2026
Same author

Quantifying the impact of vegetation changes on runoff in alpine basins using the Budyko framework integrating glacier effect.

Environmental research·2026

Related Experiment Video

Updated: Sep 3, 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

1.6K

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification.

Changnan Jiang1, Kanglong Yin1, Chunhe Xia1,2

  • 1Key Laboratory of Beijing Network Technology, Beihang University, Beijing 100191, China.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

This study introduces FedHGCDroid, a privacy-preserving method for Android malware classification using federated learning (FL). It accurately detects malicious apps while protecting user data, addressing privacy concerns in mobile security.

Keywords:
adaptivecall graphfederated learningmalware classification

Related Experiment Videos

Last Updated: Sep 3, 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

1.6K

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Mobile Security

Background:

  • Android's open-source nature attracts malware, making detection crucial.
  • Current methods require extensive data or manual input, raising privacy issues.
  • User privacy concerns challenge existing malware classification schemes.

Purpose of the Study:

  • To propose a novel, privacy-preserving Android malware classification scheme.
  • To address the limitations of current methods concerning user data privacy.
  • To develop a federated learning-based approach for collaborative malware training.

Main Methods:

  • Developed HGCDroid, a multi-dimensional model using CNN and GNN for feature extraction.
  • Implemented a federated learning (FL) framework for privacy-preserving distributed training.
  • Introduced FedAdapt, an adaptive mechanism for non-IID data distribution in FL.

Main Results:

  • FedHGCDroid effectively classifies Android malware in a privacy-protected manner.
  • The HGCDroid model accurately extracts malicious behavior features.
  • FedAdapt enhances classifier adaptability for non-IID data.

Conclusions:

  • FedHGCDroid offers a privacy-preserving solution for Android malware classification.
  • The proposed method achieves higher adaptability and accuracy compared to existing approaches.
  • This federated learning approach balances security needs with user privacy.