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Protocol-Based Multi-Ensemble Classifier for Intrusion Detection Using Packet Header Fields

https://doi.org/10.2139/ssrn.4672084

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

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

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

inferredcites →https://doi.org/10.1007/978-3-030-20257-6_40openalex-extracted

Comparison of Network Intrusion Detection Performance Using Feature Representation

inferredcites →https://doi.org/10.1007/978-3-319-07995-0_47openalex-extracted

Packet Header Anomaly Detection Using Statistical Analysis

inferredcites →https://doi.org/10.1007/978-981-15-0199-9_62openalex-extracted

Anomaly-Based Intrusion Detection System Using Support Vector Machine

inferredcites →https://doi.org/10.1007/978-981-16-5559-3_26openalex-extracted

Voting Classifier-Based Intrusion Detection for IoT Networks

inferredcites →https://doi.org/10.1007/s00500-021-05809-yopenalex-extracted

A two-stage intrusion detection approach for software-defined IoT networks

inferredcites →https://doi.org/10.1007/s00778-006-0002-5openalex-extracted

A new intrusion detection system using support vector machines and hierarchical clustering

inferredcites →https://doi.org/10.1007/s12046-020-1308-5openalex-extracted

Particle swarm optimization and feature selection for intrusion detection system

inferredcites →https://doi.org/10.1016/j.comnet.2014.08.007openalex-extracted

Traffic anomaly diagnosis in Internet backbone networks: A survey

inferredcites →https://doi.org/10.1016/j.comnet.2018.02.028openalex-extracted

A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection

inferredcites →https://doi.org/10.1016/j.compeleceng.2013.11.024openalex-extracted

A survey on feature selection methods

inferredcites →https://doi.org/10.1016/j.cose.2018.11.005openalex-extracted

Firefly algorithm based feature selection for network intrusion detection

inferredcites →https://doi.org/10.1016/j.cose.2019.05.022openalex-extracted

A novel approach to intrusion detection using SVM ensemble with feature augmentation

inferredcites →https://doi.org/10.1016/j.engappai.2021.104216openalex-extracted

Supervised feature selection techniques in network intrusion detection: A critical review

inferredcites →https://doi.org/10.1016/j.inffus.2021.02.007openalex-extracted

On learning effective ensembles of deep neural networks for intrusion detection

inferredcites →https://doi.org/10.1016/j.iot.2021.100462openalex-extracted

Intrusion detection based on Machine Learning techniques in computer networks

inferredcites →https://doi.org/10.1016/j.jisa.2019.102419openalex-extracted

Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

inferredcites →https://doi.org/10.1016/j.jnca.2012.09.004openalex-extracted

Intrusion detection system: A comprehensive review

inferredcites →https://doi.org/10.1016/j.jnca.2015.11.016openalex-extracted

A survey of network anomaly detection techniques

inferredcites →https://doi.org/10.1016/j.jocs.2017.03.006openalex-extracted

Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model

inferredcites →https://doi.org/10.1016/j.matpr.2020.06.218openalex-extracted

Review on intrusion detection using feature selection with machine learning techniques

inferredcites →https://doi.org/10.1016/j.matpr.2021.04.643openalex-extracted

Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence

inferredcites →https://doi.org/10.1016/j.procs.2020.04.133openalex-extracted

Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review

inferredcites →https://doi.org/10.1016/j.procs.2023.01.153openalex-extracted

Voting-based Ensemble Learning approach for Cyber Attacks Detection in Industrial Internet of Things

inferredcites →https://doi.org/10.1016/s1389-1286(00)00139-0openalex-extracted

The 1999 DARPA off-line intrusion detection evaluation

inferredcites →https://doi.org/10.1109/3ict51146.2020.9312002openalex-extracted

Intrusion Detection System using Feature Selection With Clustering and Classification Machine Learning Algorithms on the UNSW-NB15 dataset

inferredcites →https://doi.org/10.1109/5254.708428openalex-extracted

Support vector machines

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

An Adaptive Ensemble Machine Learning Model for Intrusion Detection

inferredcites →https://doi.org/10.1109/access.2021.3056614openalex-extracted

Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset

inferredcites →https://doi.org/10.1109/aciids.2009.59openalex-extracted

Using Rough Set and Support Vector Machine for Network Intrusion Detection System

inferredcites →https://doi.org/10.1109/icaccs.2017.8014614openalex-extracted

A survey on network attacks and Intrusion detection systems

inferredcites →https://doi.org/10.1109/iccmc.2019.8819748openalex-extracted

A Review of Machine Learning Methodologies for Network Intrusion Detection

inferredcites →https://doi.org/10.1109/icct.2012.6511281openalex-extracted

Intrusion Detection System using decision tree algorithm

inferredcites →https://doi.org/10.1109/icrtit.2011.5972283openalex-extracted

Anomaly detection system based on analysis of packet header and payload histograms

inferredcites →https://doi.org/10.1109/isco.2016.7726920openalex-extracted

PHAD: Packet header anomaly detection

inferredcites →https://doi.org/10.1109/isdfs.2018.8355327openalex-extracted

Neural network based intrusion detection systems with different training functions

inferredcites →https://doi.org/10.1109/isdfs55398.2022.9800807openalex-extracted

Network Intrusion Detection Packet Classification with the HIKARI-2021 Dataset: a study on ML Algorithms

inferredcites →https://doi.org/10.1109/siu.2019.8806451openalex-extracted

Network Anomaly Detection Using Header Information With Greedy Algorithm

inferredcites →https://doi.org/10.1109/surv.2010.021510.00088openalex-extracted

Outlier Detection Techniques for Wireless Sensor Networks: A Survey

inferredcites →https://doi.org/10.1109/tnsm.2020.3014929openalex-extracted

Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection

inferredcites →https://doi.org/10.1109/tsmc.2015.2415763openalex-extracted

Design and Analysis of Multimodel-Based Anomaly Intrusion Detection Systems in Industrial Process Automation

inferredcites →https://doi.org/10.1109/tsmcc.2008.923876openalex-extracted

Random-Forests-Based Network Intrusion Detection Systems

inferredcites →https://doi.org/10.1145/1541880.1541882openalex-extracted

Anomaly detection

inferredcites →https://doi.org/10.1155/2013/962185openalex-extracted

Design of an Evolutionary Approach for Intrusion Detection

inferredcites →https://doi.org/10.1155/2019/4318463openalex-extracted

Ensemble Classification Based on Feature Selection for Environmental Sound Recognition

inferredcites →https://doi.org/10.1155/2023/7690322openalex-extracted

Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

inferredcites →https://doi.org/10.11591/ijeecs.v25.i2.pp1151-1158openalex-extracted

Network intrusion detection system: machine learning approach

inferredcites →https://doi.org/10.1504/ijics.2020.109481openalex-extracted

A categorical survey of state-of-the-art intrusion detection system-Snort

inferredcites →https://doi.org/10.2478/jaiscr-2022-0010openalex-extracted

An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection

inferredcites →https://doi.org/10.3233/idt-2011-0117openalex-extracted

Network intrusion detection system: A machine learning approach

inferredcites →https://doi.org/10.33166/aetic.2021.05.025openalex-extracted

A Machine Learning Approach for Improving the Performance of Network Intrusion Detection Systems

inferredcites →https://doi.org/10.3390/info13070314openalex-extracted

An Effective Ensemble Automatic Feature Selection Method for Network Intrusion Detection

inferredcites →https://doi.org/10.3837/tiis.2018.10.024openalex-extracted

Feature Selection Algorithms in Intrusion Detection System: A Survey

inferredcites →https://doi.org/10.5121/ijcsit.2010.2613openalex-extracted

Network Intrusion Detection System Based On Machine Learning Algorithms

inferredcites →https://doi.org/10.52549/ijeei.v7i1.773openalex-extracted

Network anomaly detection research: a survey

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