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

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Classification of Systems-I

Classification of Systems-I

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:

Classification of Systems-II

Classification of Systems-II

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,

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...

Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

You might also read

Related Articles

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

Sort by

Same author

Distribution Patterns of Bitterness and Astringency Compounds in Different Tissues and Developmental Stages of Three Sympodial Bamboo Species.

Foods (Basel, Switzerland)·2026

Same author

Chimeric antigen receptor modified hematopoietic stem cells (CAR-HSCs) arm all immune forces for anti-tumor in mice.

Experimental hematology & oncology·2025

Same author

Research on Joint Game-Theoretic Modeling of Network Attack and Defense Under Incomplete Information.

Entropy (Basel, Switzerland)·2025

Same author

TEMPO-Oxidized Cellulose Hydrogels Loaded with Copper Nanoparticles as Highly Efficient and Reusable Catalysts for Organic Pollutant Reduction.

Gels (Basel, Switzerland)·2025

Same author

Effects of sleep quality on the risk of various long COVID symptoms among older adults following infection: an observational study.

BMC geriatrics·2025

Same author

Silicon reduces lead accumulation in Moso bamboo via immobilization and suppression of metal cation transporter genes in roots.

Tree physiology·2025

Search research articles

Related Experiment Video

Updated: Apr 18, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

CSCVAE-NID: A Conditionally Symmetric Two-Stage CVAE Framework with Cost-Sensitive Learning for Imbalanced Network

Zhenyu Wang¹, Xuejun Yu¹

¹College of Computer Science, Beijing University of Technology, Beijing 100124, China.

Entropy (Basel, Switzerland)

|November 26, 2025

Summary

This study introduces CSCVAE-NID, a novel framework to improve Network Intrusion Detection Systems (NIDSs) by addressing class imbalance and misclassification costs. It enhances detection of rare network attacks for better cybersecurity.

Keywords:

anomaly detection class imbalance conditional variational autoencoder cost-sensitive learning network intrusion detection

Related Experiment Videos

Last Updated: Apr 18, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Cybersecurity
Machine Learning
Network Security

Background:

Network Intrusion Detection Systems (NIDSs) face challenges due to complex threats and class imbalance.
Scarcity of minority attack samples and misclassification costs hinder traditional model effectiveness, causing high False Positive Rates (FPRs) and low Recall.

Purpose of the Study:

To propose a novel, conditionally symmetric two-stage framework (CSCVAE-NID) for high-performance NIDSs.
To address data imbalance and misclassification costs in network intrusion detection.

Main Methods:

Introduced a Data Augmentation Conditional Variational Autoencoder (DA-CVAE) for data-level imbalance correction by generating synthetic samples.
Developed a Cost-Sensitive Multi-Class Classification CVAE (CSMC-CVAE) that reframes classification as probabilistic distribution matching with a cost-sensitive loss function.

Main Results:

The CSCVAE-NID framework demonstrated superior performance on CICIDS-2017 and UNSW-NB15 datasets.
Achieved exceptional results in both binary and multi-class classification tasks compared to state-of-the-art methods.
The DA-CVAE module proved independent and extensible for supporting other intrusion detection methodologies.

Conclusions:

The proposed CSCVAE-NID framework effectively tackles class imbalance and misclassification costs in NIDSs.
This approach significantly enhances the performance of network intrusion detection systems.
The modular design allows for broad applicability and integration with existing and future NIDS solutions.