On the public citation graph

An Approach for the Application of a Dynamic Multi-Class Classifier for Network Intrusion Detection Systems

https://doi.org/10.3390/electronics9111759

0 signed · 53 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-05318-5_4openalex-snapshot

Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA

inferredcites →https://doi.org/10.1007/978-3-030-31239-8_17openalex-snapshot

A Cloud Based Entitlement Granting Engine

inferredcites →https://doi.org/10.1007/978-3-642-20305-3openalex-snapshot

Traffic Monitoring and Analysis

inferredcites →https://doi.org/10.1007/978-981-13-9042-5_24openalex-snapshot

Statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection

inferredcites →https://doi.org/10.1007/s10462-019-09762-zopenalex-snapshot

An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm

inferredcites →https://doi.org/10.1016/j.artint.2016.04.003openalex-snapshot

ASlib: A benchmark library for algorithm selection

inferredcites →https://doi.org/10.1016/j.asoc.2020.106301openalex-snapshot

A survey and taxonomy of the fuzzy signature-based Intrusion Detection Systems

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.cose.2017.11.004openalex-snapshot

UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSs

inferredcites →https://doi.org/10.1016/j.cose.2019.02.008openalex-snapshot

CorrCorr: A feature selection method for multivariate correlation network anomaly detection techniques

inferredcites →https://doi.org/10.1016/j.jnca.2017.11.007openalex-snapshot

Multi-classification approaches for classifying mobile app traffic

inferredcites →https://doi.org/10.1080/19393555.2015.1125974openalex-snapshot

The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set

inferredcites →https://doi.org/10.1109/access.2019.2923640openalex-snapshot

An Adaptive Ensemble Machine Learning Model for Intrusion Detection

inferredcites →https://doi.org/10.1109/access.2019.2963407openalex-snapshot

Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies

inferredcites →https://doi.org/10.1109/access.2020.3009843openalex-snapshot

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

inferredcites →https://doi.org/10.1109/asicon47005.2019.8983598openalex-snapshot

SVM Based Network Intrusion Detection for the UNSW-NB15 Dataset

inferredcites →https://doi.org/10.1109/comst.2018.2847722openalex-snapshot

A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection

inferredcites →https://doi.org/10.1109/desec.2017.8073860openalex-snapshot

Development of a network intrusion detection system using Apache Hadoop and Spark

inferredcites →https://doi.org/10.1109/iccci.2012.6158779openalex-snapshot

An efficient clustering algorithm based on Z-Score ranking method

inferredcites →https://doi.org/10.1109/icccn.2019.8847179openalex-snapshot

Supervised Machine Learning Techniques for Efficient Network Intrusion Detection

inferredcites →https://doi.org/10.1109/icosp.2006.345752openalex-snapshot

Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding

inferredcites →https://doi.org/10.1109/icscee.2018.8538395openalex-snapshot

Anomaly detection in cyber security attacks on networks using MLP deep learning

inferredcites →https://doi.org/10.1109/isspit47144.2019.9001867openalex-snapshot

Development of an Efficient Network Intrusion Detection Model Using Extreme Gradient Boosting (XGBoost) on the UNSW-NB15 Dataset

inferredcites →https://doi.org/10.1109/milcis.2015.7348942openalex-snapshot

UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

inferredcites →https://doi.org/10.1109/sege.2019.8859773openalex-snapshot

Modeling Network Intrusion Detection System Using Feed-Forward Neural Network Using UNSW-NB15 Dataset

inferredcites →https://doi.org/10.1109/tpds.2018.2876844openalex-snapshot

Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network Analytics

inferredcites →https://doi.org/10.1109/ubmk.2017.8093473openalex-snapshot

Big data analytics for network anomaly detection from netflow data

inferredcites →https://doi.org/10.1111/coin.12220openalex-snapshot

Empirical study on multiclass classification‐based network intrusion detection

inferredcites →https://doi.org/10.1162/evco_a_00242openalex-snapshot

Automated Algorithm Selection: Survey and Perspectives

inferredcites →https://doi.org/10.1609/aaai.v24i1.7565openalex-snapshot

Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection

inferredcites →https://doi.org/10.3115/1654494.1654500openalex-snapshot

Betterk-best parsing

inferredcites →https://doi.org/10.3390/app10103430openalex-snapshot

Efficient Distributed Preprocessing Model for Machine Learning-Based Anomaly Detection over Large-Scale Cybersecurity Datasets

inferredcites →https://doi.org/10.3390/info10040122openalex-snapshot

A Survey of Deep Learning Methods for Cyber Security

inferredcites →https://doi.org/10.48550/arxiv.1208.3719openalex-snapshot

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

inferredcites →https://doi.org/10.48550/arxiv.1806.03517openalex-snapshot

A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems

What points at this work

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

inferred← citeshttps://doi.org/10.1002/9781394209835.ch13openalex-snapshot

Detection of IoT Attacks Using Hybrid RNN‐DBN Model

inferred← citeshttps://doi.org/10.1007/978-3-031-05752-6_1openalex-snapshot

New Advancements in Cybersecurity: A Comprehensive Survey

inferred← citeshttps://doi.org/10.1007/978-3-031-94898-5_45openalex-snapshot

A Two-Stage Deep Learning Framework Based on Device Address Features for Network Intrusion Detection

inferred← citeshttps://doi.org/10.1007/s10586-024-04952-zopenalex-snapshot

Enhanced anomaly-based network intrusion detection leveraging a modified picture fuzzy clustering approach

inferred← citeshttps://doi.org/10.1007/s11227-025-07123-yopenalex-snapshot

Enhancing network intrusion detection by employing Mondrian forests to achieve multiple attack classification

inferred← citeshttps://doi.org/10.1109/access.2024.3473289openalex-snapshot

Network Intrusion Detection: An IoT and Non IoT-Related Survey

inferred← citeshttps://doi.org/10.1109/aiiot58121.2023.10174448openalex-snapshot

A Recent Survey on Intrusion Detection Methods for Wireless Networks

inferred← citeshttps://doi.org/10.1109/ictc55196.2022.9952376openalex-snapshot

IoT Network Intrusion Detection with Ensemble Learners

inferred← citeshttps://doi.org/10.1109/wccst67302.2026.11496094openalex-snapshot

Implementing Artificial Intelligence Techniques for Zero-Trust Intrusion Detection in Cloud Environments

inferred← citeshttps://doi.org/10.20868/upm.thesis.69604openalex-snapshot

Optimization of intrusion detection systems based on machine learning for cybersecurity by boosting feature engineering and model selection techniques

inferred← citeshttps://doi.org/10.21203/rs.3.rs-2357212/v1openalex-snapshot

A Hybrid PCA-MAO Based LSTM Model for Intrusion Detection in IoT Environments

inferred← citeshttps://doi.org/10.32604/iasc.2023.039255openalex-snapshot

A Novel Ensemble Learning System for Cyberattack Classification

inferred← citeshttps://doi.org/10.32604/iasc.2023.039687openalex-snapshot

Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

inferred← citeshttps://doi.org/10.3390/e23050618openalex-snapshot

A Compression-Based Method for Detecting Anomalies in Textual Data

inferred← citeshttps://doi.org/10.3390/electronics11182803openalex-snapshot

Advanced Cybersecurity Services Design

inferred← citeshttps://doi.org/10.3390/s22051831openalex-snapshot

Multi-Aspect Based Approach to Attack Detection in IoT Clouds

inferred← citeshttps://doi.org/10.37917/ijeee.20.2.13openalex-snapshot

Advancements and Challenges in Hand Gesture Recognition: A Comprehensive Review

inferred← citeshttps://doi.org/10.5753/latinoware.2025.16318openalex-snapshot

Comparative Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection in Electric Vehicle Systems

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.