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EmSM: Ensemble Mixed Sampling Method for Classifying Imbalanced Intrusion Detection Data

https://doi.org/10.3390/electronics11091346

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11538059_91openalex-snapshot

Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

inferredcites →https://doi.org/10.1007/978-3-030-24344-9_8openalex-snapshot

Expansion of Cyber Attack Data from Unbalanced Datasets Using Generative Adversarial Networks

inferredcites →https://doi.org/10.1007/978-3-319-10422-5_21openalex-snapshot

Anomalous Web Payload Detection: Evaluating the Resilience of 1-Grams Based Classifiers

inferredcites →https://doi.org/10.1007/978-3-319-98074-4openalex-snapshot

Learning from Imbalanced Data Sets

inferredcites →https://doi.org/10.1007/978-3-540-39804-2_12openalex-snapshot

SMOTEBoost: Improving Prediction of the Minority Class in Boosting

inferredcites →https://doi.org/10.1007/978-3-642-25975-3_30openalex-snapshot

A New Over-Sampling Approach: Random-SMOTE for Learning from Imbalanced Data Sets

inferredcites →https://doi.org/10.1007/s00779-019-01332-yopenalex-snapshot

GAN-based imbalanced data intrusion detection system

inferredcites →https://doi.org/10.1016/j.comnet.2020.107315openalex-snapshot

An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset

inferredcites →https://doi.org/10.1016/j.ins.2014.08.051openalex-snapshot

SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering

inferredcites →https://doi.org/10.1016/j.procs.2020.04.085openalex-snapshot

Siam-IDS: Handling class imbalance problem in Intrusion Detection Systems using Siamese Neural Network

inferredcites →https://doi.org/10.1108/k-04-2020-0214openalex-snapshot

Hybrid ensemble learning approaches to customer churn prediction

inferredcites →https://doi.org/10.1109/access.2020.3031892openalex-snapshot

Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive Stacked Autoencoder

inferredcites →https://doi.org/10.1109/access.2020.3048198openalex-snapshot

Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning

inferredcites →https://doi.org/10.1109/compcomm.2016.7924886openalex-snapshot

SMOTE-NCL: A re-sampling method with filter for network intrusion detection

inferredcites →https://doi.org/10.1109/compcomm.2017.8322749openalex-snapshot

A novel region adaptive SMOTE algorithm for intrusion detection on imbalanced problem

inferredcites →https://doi.org/10.1109/icdm.2001.989527openalex-snapshot

Evaluating boosting algorithms to classify rare classes: comparison and improvements

inferredcites →https://doi.org/10.1109/iwscn.2011.6827720openalex-snapshot

Applying feature selection to payload-based Web Application Firewalls

inferredcites →https://doi.org/10.1109/tnse.2020.3004312openalex-snapshot

AESMOTE: Adversarial Reinforcement Learning With SMOTE for Anomaly Detection

inferredcites →https://doi.org/10.1109/tsmc.1976.4309452openalex-snapshot

Two Modifications of CNN

inferredcites →https://doi.org/10.1145/3386723.3387847openalex-snapshot

A Review on Cybersecurity

inferredcites →https://doi.org/10.1145/3409501.3409516openalex-snapshot

A Network Intrusion Detection Model based on the Combination of ReliefF and Borderline-SMOTE

inferredcites →https://doi.org/10.11591/ijins.v1i3.704openalex-snapshot

Pattern Based Network Security Using Semi-supervised Learning

inferredcites →https://doi.org/10.1186/s40537-019-0192-5openalex-snapshot

Survey on deep learning with class imbalance

inferredcites →https://doi.org/10.1186/s42400-019-0038-7openalex-snapshot

Survey of intrusion detection systems: techniques, datasets and challenges

inferredcites →https://doi.org/10.14257/ijsia.2018.12.4.02openalex-snapshot

A Survey of Cyber Security Approaches for Attack Detection, Prediction, and Prevention

inferredcites →https://doi.org/10.21533/pen.v7i3.635openalex-snapshot

Survey on intrusion detection systems based on deep learning

inferredcites →https://doi.org/10.33832/ijsia.2019.13.4.05openalex-snapshot

An Ensemble Classification Approach with Selective Under and Over Sampling of Imbalance Intrusion Detection Dataset

inferredcites →https://doi.org/10.3390/electronics10101148openalex-snapshot

PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-18843-5_16openalex-snapshot

Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121710openalex-snapshot

An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

inferred← citeshttps://doi.org/10.1016/j.ijar.2023.02.006openalex-snapshot

Re-sampling of multi-class imbalanced data using belief function theory and ensemble learning

inferred← citeshttps://doi.org/10.1038/s41598-024-75703-8openalex-snapshot

A rate of penetration (ROP) prediction method based on improved dung beetle optimization algorithm and BiLSTM-SA

inferred← citeshttps://doi.org/10.1109/access.2023.3328535openalex-snapshot

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

inferred← citeshttps://doi.org/10.1109/candarw64572.2024.00016openalex-snapshot

An Improved Method for Detecting EDoS Attacks in the Cloud With Hyperparameter Optimization and Metaheuristic Algorithms

inferred← citeshttps://doi.org/10.1109/icesc57686.2023.10193625openalex-snapshot

Deep Learning with Conditional Generative Adversarial Network Based Intrusion Detection System on Balanced Data

inferred← citeshttps://doi.org/10.1109/ieacon57683.2023.10370430openalex-snapshot

Assessing the Effectiveness of Oversampling and Undersampling Techniques for Intrusion Detection on an Imbalanced Dataset

inferred← citeshttps://doi.org/10.1109/safeprocess58597.2023.10295926openalex-snapshot

Network Intrusion Detection Based on Graph Neural Network and Ensemble Learning

inferred← citeshttps://doi.org/10.1117/12.2655187openalex-snapshot

A method of unbalanced twin auxiliary classifiers GAN for network intrusion based mutual information

inferred← citeshttps://doi.org/10.3390/electronics12051168openalex-snapshot

Fusion Model for Classification Performance Optimization in a Highly Imbalance Breast Cancer Dataset

inferred← citeshttps://doi.org/10.3390/math12030432openalex-snapshot

Damage Detection in Structures by Using Imbalanced Classification Algorithms

inferred← citeshttps://doi.org/10.3390/s24196335openalex-snapshot

Focal Causal Temporal Convolutional Neural Networks: Advancing IIoT Security with Efficient Detection of Rare Cyber-Attacks

inferred← citeshttps://doi.org/10.3390/sym16070850openalex-snapshot

A Network Intrusion Detection Method Based on Bagging Ensemble

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