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

Classification of Systems-I01:26

Classification of Systems-I

668
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:
668
Classification of Systems-II01:31

Classification of Systems-II

562
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,
562
Detection of Black Holes01:10

Detection of Black Holes

2.6K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.6K
Classification of Signals01:30

Classification of Signals

1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
Aggregates Classification01:29

Aggregates Classification

1.1K
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...
1.1K
Force Classification01:22

Force Classification

2.6K
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,...
2.6K

You might also read

Related Articles

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

Sort by
Same author

GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.

Scientific reports·2026
Same author

KANWhisper: leveraging learnable activation functions for interpretable and efficient arabic automatic speech recognition.

Scientific reports·2026
Same author

MultiScaleKANNet: a hybrid CNN-KAN-transformer architecture for radiographic bone-loss risk stratification from knee X-rays.

Scientific reports·2026
Same author

FedEmoNet: Privacy-preserving federated learning with TCN-Transformer fusion for cross-corpus speech emotion recognition.

PloS one·2026
Same author

A privacy-preserving cloud storage framework with hybrid encryption, homomorphic keyword search, and blockchain-based integrity verification.

Scientific reports·2026
Same author

Dynamic fog node placement optimization using adaptive dynamic pufferfish optimization for real-time IoT networks.

Scientific reports·2026
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Mar 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection.

Mohammed Tawfik1, Hussam Tarazi2, Ahmad Dalalah3

  • 1Faculty of Computer and Information Technology, Sana'a University, Sana'a, Yemen. m.tawfik@su.edu.ye.

Scientific Reports
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive few-shot learning framework for Android malware detection, overcoming data scarcity and concept drift. It achieves high accuracy with minimal data, offering interpretable and stable threat identification.

Keywords:
Android malware detectionConcept drift detectionFew-shot learningPrototypical networksQuantum machine learning

Related Experiment Videos

Last Updated: Mar 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Area of Science:

  • Cybersecurity and Machine Learning
  • Mobile Security
  • Artificial Intelligence

Background:

  • Android malware detection faces challenges like data scarcity for new threats, high-dimensional features, and concept drift from evolving attacks.
  • Traditional machine learning methods require large labeled datasets and frequent retraining, hindering practical deployment against rapidly emerging malware.
  • Existing systems struggle with efficiency and adaptability in dynamic threat landscapes.

Purpose of the Study:

  • To propose an adaptive few-shot malware classification framework addressing data scarcity and concept drift in Android malware detection.
  • To enhance classification accuracy and efficiency using advanced machine learning techniques.
  • To provide interpretable and stable malware detection solutions.

Main Methods:

  • Implemented an adaptive few-shot malware classification framework integrating CatBoost-based feature selection, prototypical networks with episodic meta-learning, and quantum-enhanced classification.
  • Incorporated concept drift detection and explainable AI (XAI) analysis using SHAP and LIME for model interpretability.
  • Utilized CatBoost for significant dimensionality reduction (e.g., 99.46% on CCCS-CIC-AndMal-2020) while preserving discriminative features.

Main Results:

  • Achieved state-of-the-art performance with 99.70% accuracy on CCCS-CIC-AndMal-2020 (15 families) and 99.33% accuracy on KronoDroid (binary classification).
  • Demonstrated effective classification with as few as 5 support samples per class, significantly outperforming existing methods (0.70-9.70% improvement).
  • Showcased robust temporal stability with minimal accuracy degradation (max 0.24%) and identified key discriminative features like file descriptor manipulation.

Conclusions:

  • Few-shot prototypical learning combined with intelligent feature selection offers an effective paradigm for practical Android malware detection.
  • The framework requires minimal annotation, provides interpretable decisions, and ensures stable long-term performance.
  • This approach significantly advances the ability to detect and classify emerging Android malware families efficiently and accurately.