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Network Intrusion Detection Using Clustering and Gradient Boosting

https://doi.org/10.1109/icccnt.2018.8494186
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-642-20757-0_4openalex-snapshot
UNADA: Unsupervised Network Anomaly Detection Using Sub-space Outliers Ranking
inferredcites →https://doi.org/10.1016/j.patrec.2005.11.007openalex-snapshot
A clustering-based method for unsupervised intrusion detections
inferredcites →https://doi.org/10.1016/s0167-9473(01)00065-2openalex-snapshot
Stochastic gradient boosting
inferredcites →https://doi.org/10.1109/ccece.2003.1226084openalex-snapshot
Y-means: a clustering method for intrusion detection
inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-snapshot
A detailed analysis of the KDD CUP 99 data set
inferredcites →https://doi.org/10.1109/csac.1998.738566openalex-snapshot
NetSTAT: a network-based intrusion detection approach
inferredcites →https://doi.org/10.1109/discex.2001.932187openalex-snapshot
Statistical process control for computer intrusion detection
inferredcites →https://doi.org/10.1109/ftdcs.2004.1316613openalex-snapshot
A fuzzy data mining based intrusion detection model
inferredcites →https://doi.org/10.1109/fuzzy.2004.1375759openalex-snapshot
Support vector classifiers and network intrusion detection
inferredcites →https://doi.org/10.1109/icdcs.2010.45openalex-snapshot
Sketch-Based Streaming PCA Algorithm for Network-Wide Traffic Anomaly Detection
inferredcites →https://doi.org/10.1109/icmla.2017.00-36openalex-snapshot
Catch It If You Can: Real-Time Network Anomaly Detection with Low False Alarm Rates
inferredcites →https://doi.org/10.1109/icmlc.2002.1176698openalex-snapshot
Using data mining to discover signatures in network-based intrusion detection
inferredcites →https://doi.org/10.1109/icnp.1998.723726openalex-snapshot
Statistical anomaly detection for link-state routing protocols
inferredcites →https://doi.org/10.1109/icstm.2015.7225395openalex-snapshot
Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection
inferredcites →https://doi.org/10.1109/ijcnn.1998.682263openalex-snapshot
Neural networks applied in intrusion detection systems
inferredcites →https://doi.org/10.1109/isias.2010.5604193openalex-snapshot
Discriminative multinomial Naïve Bayes for network intrusion detection
inferredcites →https://doi.org/10.1109/skima.2014.7083539openalex-snapshot
Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs
inferredcites →https://doi.org/10.1109/sp.2010.25openalex-snapshot
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
inferredcites →https://doi.org/10.1109/spaces.2015.7058223openalex-snapshot
Performance analysis of NSL-KDD dataset using ANN
inferredcites →https://doi.org/10.1109/surv.2013.052213.00046openalex-snapshot
Network Anomaly Detection: Methods, Systems and Tools
inferredcites →https://doi.org/10.1109/tnsm.2016.2627340openalex-snapshot
Online and Scalable Unsupervised Network Anomaly Detection Method
inferredcites →https://doi.org/10.1109/tse.1987.232894openalex-snapshot
An Intrusion-Detection Model
inferredcites →https://doi.org/10.1109/tsmcc.2004.843247openalex-snapshot
Top-Down Induction of Decision Trees Classifiers—A Survey
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.1214/aos/1016218223openalex-snapshot
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
inferredcites →https://doi.org/10.4018/9781599041681.ch007openalex-snapshot
Intrusion Detection System

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.5048254openalex-extracted
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
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Machine Learning–Based Security in Cloud Database—A Survey
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A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks
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UIoTN‐PMSE: Ubiquitous IoT network‐based predictive modeling in smart environment
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An improved ensemble based intrusion detection technique using XGBoost
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A Review Paper on Network Intrusion Detection System
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A Stacked Ensemble Method with Adaptive Attribute Selection to Detect DDoS Attack in Cloud-Assisted WBAN
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A Sorting-AB Approach for an Enhanced IDS in Cloud Environment
inferred← citeshttps://doi.org/10.1007/978-981-15-7130-5_38openalex-snapshot
Multilayer Perceptron and Genetic Algorithm-Based Intrusion Detection Framework for Cloud Environment
inferred← citeshttps://doi.org/10.1007/978-981-96-3381-4_48openalex-snapshot
A Comparative Study for New-Age Attacks in a Machine Learning-Based Intrusion Detection System
inferred← citeshttps://doi.org/10.1007/978-981-96-7400-8_7openalex-snapshot
Improved Vulnerability Detection in Applications Using Machine Learning
inferred← citeshttps://doi.org/10.1007/s10115-025-02366-wopenalex-snapshot
Cybersecurity in the AI era: analyzing the impact of machine learning on intrusion detection
inferred← citeshttps://doi.org/10.1007/s10462-021-10037-9openalex-snapshot
A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
inferred← citeshttps://doi.org/10.1007/s10489-020-01886-yopenalex-snapshot
I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems
inferred← citeshttps://doi.org/10.1007/s10664-025-10766-3openalex-snapshot
On detection latencies of network intrusion detectors – discussion and application
inferred← citeshttps://doi.org/10.1007/s10668-022-02299-2openalex-snapshot
A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting
inferred← citeshttps://doi.org/10.1007/s11227-024-06345-wopenalex-snapshot
A network intrusion detection system based on deep learning in the IoT
inferred← citeshttps://doi.org/10.1007/s13369-019-04178-xopenalex-snapshot
An Adaptive Threshold-Based Attribute Selection to Classify Requests Under DDoS Attack in Cloud-Based Systems
inferred← citeshttps://doi.org/10.1007/s40860-020-00126-xopenalex-snapshot
A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model
inferred← citeshttps://doi.org/10.1007/s41870-020-00583-wopenalex-snapshot
Attack and intrusion detection in cloud computing using an ensemble learning approach
inferred← citeshttps://doi.org/10.1016/j.cose.2019.06.013openalex-snapshot
Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126718openalex-snapshot
A comparative analysis of machine learning techniques for detecting probing attack with SHAP algorithm
inferred← citeshttps://doi.org/10.1016/j.scs.2021.102994openalex-snapshot
A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets
inferred← citeshttps://doi.org/10.1038/s41598-025-33242-wopenalex-snapshot
A hybrid improved binary GWO-PSO with random forest (IBGWO-PSO-RF) based intrusion detection model for large-scale attacks in IoT environment
inferred← citeshttps://doi.org/10.1063/5.0155605openalex-snapshot
Impact of novel machine learning approaches on intrusion detection system
inferred← citeshttps://doi.org/10.1063/5.0180641openalex-snapshot
Intrusion detection system based on machine learning models: An empirical analysis
inferred← citeshttps://doi.org/10.1063/5.0285864openalex-snapshot
Using a hybrid machine learning model on network intrusion detection system
inferred← citeshttps://doi.org/10.1109/access.2020.3026044openalex-snapshot
A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System
inferred← citeshttps://doi.org/10.1109/access.2022.3171660openalex-snapshot
An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System
inferred← citeshttps://doi.org/10.1109/access.2023.3296311openalex-snapshot
5G Aviation Networks Using Novel AI Approach for DDoS Detection
inferred← citeshttps://doi.org/10.1109/acoit62457.2024.10939303openalex-snapshot
A Collaborative Framework for Intrusion Detection in Cloud Computing Based on HLA-CNN-BiLSTM-SVM Model
inferred← citeshttps://doi.org/10.1109/aimla59606.2024.10531381openalex-snapshot
Network Intrusion Detection System Using Optimized Feature Selection
inferred← citeshttps://doi.org/10.1109/conecct62155.2024.10677096openalex-snapshot
Improving Intrusion Detection System By Utilizing Stacking Based Convolutional Neural Network Ensemble Classifier
inferred← citeshttps://doi.org/10.1109/icccis51004.2021.9397121openalex-snapshot
A Review on Intrusion Detection System using Machine Learning Techniques
inferred← citeshttps://doi.org/10.1109/icecaa58104.2023.10212144openalex-snapshot
Enhanced Protection for Information and Network using Intrusion Detection System
inferred← citeshttps://doi.org/10.1109/iciet57285.2023.10220795openalex-snapshot
A Comprehensive Survey on Ensemble Machine Learning Approaches for Detection of Intrusion in IoT Networks
inferred← citeshttps://doi.org/10.1109/icmcsi64620.2025.10883531openalex-snapshot
Network Anomaly Detection Using Similarity-Aware Ensemble Learning
inferred← citeshttps://doi.org/10.1109/icodt262145.2024.10740248openalex-snapshot
A Host-based Intrusion Detection: Using Signature-based and AI-driven Anomaly Detection for Enhanced Cybersecurity*
inferred← citeshttps://doi.org/10.1109/icosec49089.2020.9215333openalex-snapshot
Intrusion Detection System using Machine Learning Techniques: A Review
inferred← citeshttps://doi.org/10.1109/ictc55196.2022.9952376openalex-snapshot
IoT Network Intrusion Detection with Ensemble Learners
inferred← citeshttps://doi.org/10.1109/issre62328.2024.00015openalex-snapshot
Detection Latencies of Anomaly Detectors - An Overlooked Perspective?
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Intrusion Detection System using Semi-Supervised Learning with Adversarial Auto-encoder
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Beyond Boundaries: Innovating Network Protection Through the Fusion of Boosting and Smote Techniques
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Design of intrusion detection systems on the internet of things infrastructure using machine learning algorithms
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General-purpose Unsupervised Cyber Anomaly Detection via Non-negative Tensor Factorization
inferred← citeshttps://doi.org/10.1145/3568562.3568660openalex-snapshot
Leveraging AI-Driven Realtime Intrusion Detection by Using WGAN and XGBoost
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An Analytical Approach on Artificial Intelligence Based Models for Anomaly Detection in Sensor Networks
inferred← citeshttps://doi.org/10.1155/2021/5844728openalex-snapshot
Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems
inferred← citeshttps://doi.org/10.1186/s12938-021-00867-7openalex-snapshot
Artificial intelligence-based nomogram for small-incision lenticule extraction
inferred← citeshttps://doi.org/10.12691/jcsa-8-2-2openalex-snapshot
Detecting Malicious DNS over HTTPS Traffic in Domain Name System using Machine Learning Classifiers
inferred← citeshttps://doi.org/10.12720/jait.14.6.1345-1353openalex-snapshot
Leveraging the Training Data Partitioning to Improve Events Characterization in Intrusion Detection Systems
inferred← citeshttps://doi.org/10.14419/jacst.v9i1.30992openalex-snapshot
Evaluating the performance of machine learning algorithms for network intrusion detection systems in the internet of things infrastructure
inferred← citeshttps://doi.org/10.17714/gumusfenbil.1268504openalex-snapshot
Makine öğrenmesi algoritmaları ile deprem katalogları kullanılarak deprem tahmini
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Vigorous IDS on Nefarious Operations and Threat Analysis Using Ensemble Machine Learning
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Network Anomaly Detection using Autoencoder on Various Datasets: A Comprehensive Review
inferred← citeshttps://doi.org/10.2174/9789815256710124010011openalex-snapshot
Improving Performance of Machine LearningBased Intrusion Detection System Using Simple Statistical Techniques in Feature Selection
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An Artificial Intelligence-based Proactive Network Forensic Framework
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Intrusion Detection Using Hidden Markov Model and XGBoost Algorithm
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Using Ensemble Techniques Based on Machine and Deep Learning for Solving Intrusion Detection Problems: A Survey
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MANET: A Survey on Machine Learning-Based Intrusion Detection Approaches
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A Local Feature Engineering Strategy to Improve Network Anomaly Detection
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Smart Greenhouse Based on ANN and IOT
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A Review on Intrusion Detection Using Machine Learning Techniques
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Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai
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Monitoring Network Traffic for Suspicious Activity
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Predicting the Corrosion Rate of Al and Mg Alloys Coated by Plasma Spraying Method with Machine Learning
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