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

Genetic Drift03:33

Genetic Drift

40.1K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
40.1K
Classification of Systems-II01:31

Classification of Systems-II

192
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,
192
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

680
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
680
Classification of Systems-I01:26

Classification of Systems-I

236
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:
236
Genetic Screens02:46

Genetic Screens

5.0K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.0K
Classification of Signals01:30

Classification of Signals

574
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...
574

You might also read

Related Articles

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

Sort by
Same author

Systematic Review of Commercially Available Clinical CMUT-Based Systems for Use in Medical Ultrasound Imaging: Products, Applications, and Performance.

Sensors (Basel, Switzerland)·2025
Same author

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.

Sensors (Basel, Switzerland)·2024
Same author

HLD-DDoSDN: High and low-rates dataset-based DDoS attacks against SDN.

PloS one·2024
Same author

Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks.

Sensors (Basel, Switzerland)·2023
Same author

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback.

Sensors (Basel, Switzerland)·2023
Same author

Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 2, 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

Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning

Methaq A Shyaa1, Zurinahni Zainol1, Rosni Abdullah1

  • 1School of Computer Sciences, Universiti Sains Malaysia, USM, Gelugor 11800, Pulau Penang, Malaysia.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces novel Genetic Programming Combiner (GPC) variants to effectively handle concept drift in data streams for intrusion detection systems. The enhanced GPC models significantly outperform traditional methods, demonstrating improved accuracy and adaptability.

Keywords:
concept driftgenetic programming combinerincremental learningintrusion detectionstream data classificationtransfer learning

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

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

Published on: May 10, 2024

830
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

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

Published on: February 9, 2024

1.5K

Related Experiment Videos

Last Updated: Aug 2, 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
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

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

Published on: May 10, 2024

830
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

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

Published on: February 9, 2024

1.5K

Area of Science:

  • Machine Learning
  • Data Stream Mining
  • Network Intrusion Detection Systems

Background:

  • Concept drift (CD) poses a challenge for data stream classification in intrusion detection systems (IDS) due to changing data distributions.
  • Traditional static machine learning models in Genetic Programming Combiner (GPC) struggle to adapt to various CD variants (incremental, gradual, recurrent, sudden, blip).

Purpose of the Study:

  • To propose an extended Genetic Programming Combiner (GPC) framework designed to effectively address concept drift in data streams.
  • To introduce novel GPC variants incorporating online sequential extreme learning machine (OSELM) alternatives and adaptive components for improved CD handling.

Main Methods:

  • Replaced static classifiers with online sequential extreme learning machine (OSELM), feature adaptive OSELM (FA-OSELM), and knowledge preservation OSELM (KP-OSELM).
  • Integrated data balancing and classifier update mechanisms into the GPC framework.
  • Developed three novel GPC variants: GPC-KOS (for KP-OSELM), GPC-FOS (for FA-OSELM), and GPC-OS (for OSELM).

Main Results:

  • The novel GPC variants, particularly GPC-KOS and GPC-FOS, significantly outperformed the traditional GPC and other state-of-the-art methods in handling concept drift.
  • Transfer learning and memory features in the proposed models contributed to effective adaptation to most CD types.
  • Applied to real-world datasets (KDD Cup '99, CICIDS-2017, CSE-CIC-IDS-2018, ISCX '12), GPC-FOS and GPC-KOS achieved maximum accuracy rates of 98% and 100%, respectively.

Conclusions:

  • The proposed extended GPC framework offers a robust solution for data stream classification in IDS, effectively managing various concept drift scenarios.
  • GPC-KOS and GPC-FOS demonstrate superior performance, highlighting the benefits of incorporating online learning and adaptive strategies.
  • While effective for most CD types, the proposed variants showed limitations in addressing 'blip' concept drift.