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Preprocessing Impact Analysis for Machine Learning-Based Network Intrusion Detection

https://doi.org/10.35377/saucis...1223054

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What this work says

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

inferredcites →https://doi.org/10.1002/ett.4150openalex-snapshot

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

inferredcites →https://doi.org/10.1002/sec.508openalex-snapshot

Network intrusion detection using hybrid binary PSO and random forests algorithm

inferredcites →https://doi.org/10.1007/978-81-322-2695-6_45openalex-snapshot

Improving the Accuracy of Intrusion Detection Using GAR-Forest with Feature Selection

inferredcites →https://doi.org/10.1007/s10844-015-0388-xopenalex-snapshot

Two-tier network anomaly detection model: a machine learning approach

inferredcites →https://doi.org/10.1007/s12530-020-09364-zopenalex-snapshot

Developing new deep-learning model to enhance network intrusion classification

inferredcites →https://doi.org/10.1016/j.asoc.2019.105524openalex-snapshot

Investigating the impact of data normalization on classification performance

inferredcites →https://doi.org/10.1016/j.cose.2017.06.005openalex-snapshot

A GA-LR wrapper approach for feature selection in network intrusion detection

inferredcites →https://doi.org/10.1016/j.icte.2020.12.006openalex-snapshot

An efficient feature reduction method for the detection of DoS attack

inferredcites →https://doi.org/10.1016/j.icte.2021.04.012openalex-snapshot

Feature selection for intrusion detection system in Internet-of-Things (IoT)

inferredcites →https://doi.org/10.1016/j.neucom.2014.08.010openalex-snapshot

Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis

inferredcites →https://doi.org/10.1023/a:1012487302797openalex-snapshot

Gene Selection for Cancer Classification using Support Vector Machines

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/10.1088/1757-899x/928/3/032027openalex-snapshot

Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset

inferredcites →https://doi.org/10.1109/access.2017.2762418openalex-snapshot

A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

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/icetet.2010.134openalex-snapshot

On Performing Classification Using SVM with Radial Basis and Polynomial Kernel Functions

inferredcites →https://doi.org/10.1109/isie.2017.8001537openalex-snapshot

Feature selection in UNSW-NB15 and KDDCUP'99 datasets

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/10.1145/3442445openalex-snapshot

Toward a Better Understanding of “Cybersecurity”

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

Survey of intrusion detection systems: techniques, datasets and challenges

inferredcites →https://doi.org/10.3233/apc210005openalex-snapshot

An Effective Comparative Analysis of Data Preprocessing Techniques in Network Intrusion Detection System Using Deep Neural Networks

inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot

R: A Language and Environment for Statistical Computing

inferredcites →https://doi.org/10.3390/electronics9071151openalex-snapshot

Packet Preprocessing in CNN-Based Network Intrusion Detection System

inferredcites →https://doi.org/10.3390/sym12101695openalex-snapshot

SAAE-DNN: Deep Learning Method on Intrusion Detection

inferredcites →https://doi.org/10.35940/ijrte.e6580.018520openalex-snapshot

A Novel Framework for NIDS Throuh Fast Knn Classifier on CICIDS 2017 Dataset

inferredcites →https://doi.org/10.5220/0006639801080116openalex-snapshot

Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

What points at this work

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

inferred← citeshttps://doi.org/10.1002/eng2.70419openalex-snapshot

Guardians of the Network: An Ensemble Learning Framework With Adversarial Alignment for Evasive Cyber Threat Detection

inferred← citeshttps://doi.org/10.1007/s10586-026-06323-2openalex-snapshot

Enhancing intrusion detection using MAG: a shallow multilayer perceptron tuned with Adam and grey wolf optimization

inferred← citeshttps://doi.org/10.1007/s10922-024-09813-zopenalex-snapshot

Evaluating the Impact of Data Preprocessing Techniques on the Performance of Intrusion Detection Systems

inferred← citeshttps://doi.org/10.1007/s44248-025-00088-6openalex-snapshot

Predicting coronary artery disease with ensemble-based feature selection and grid search-tuned SVM under class imbalance conditions

inferred← citeshttps://doi.org/10.1038/s41598-025-94624-8openalex-snapshot

Advanced intrusion detection in internet of things using graph attention networks

inferred← citeshttps://doi.org/10.1088/2631-8695/ae3cdbopenalex-snapshot

Network intrusion detection techniques based on improved multi-scale 1D convolutional neural networks

inferred← citeshttps://doi.org/10.1088/2631-8695/ae47a2openalex-snapshot

An integrated multi-criteria decision-making framework for assessing and ranking intrusion detection systems

inferred← citeshttps://doi.org/10.1109/access.2023.3349287openalex-snapshot

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

inferred← citeshttps://doi.org/10.1109/inspect63485.2024.10896183openalex-snapshot

Towards the Machine Learning Techniques Based Network Intrusion Detection System

inferred← citeshttps://doi.org/10.1177/10567895251380211openalex-snapshot

Ensemble learning-based artificial intelligence approaches for predicting low-cycle fatigue life for aluminum alloys

inferred← citeshttps://doi.org/10.17350/hjse19030000366openalex-snapshot

Investigating the Impact of One-Hot Encoding on Classification Performance and Time Complexity for Efficient Network Intrusion Detection

inferred← citeshttps://doi.org/10.31326/jisa.v7i1.2056openalex-snapshot

Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review

inferred← citeshttps://doi.org/10.52783/cana.v31.666openalex-snapshot

Statistical Implementation for SD-RNN Model for Multi-Class Classification for Network Intrusion Detection System

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