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A Semi-Boosted Nested Model With Sensitivity-Based Weighted Binarization for Multi-Domain Network Intrusion Detection

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What this work says

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

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Hybrid intelligent systems for detecting network intrusions

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Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

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Semi-Naïve Bayesian Method for Network Intrusion Detection System

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Bagging Ensemble Selection

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Application of Bagging, Boosting and Stacking to Intrusion Detection

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Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction

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TermID: a distributed swarm intelligence-based approach for wireless intrusion detection

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Applying correlation to enhance boosting technique using genetic programming as base learner

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An analysis of diversity measures

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An ensemble uncertainty aware measure for directed hill climbing ensemble pruning

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Anomaly based intrusion detection for 802.11 networks with optimal features using SVM classifier

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Considering diversity and accuracy simultaneously for ensemble pruning

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Intrusion detection using neural based hybrid classification methods

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Feature deduction and ensemble design of intrusion detection systems

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A survey of intrusion detection systems based on ensemble and hybrid classifiers

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A novel intrusion detection system based on hierarchical clustering and support vector machines

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MARK-ELM: Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection

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A feature reduced intrusion detection system using ANN classifier

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Ensemble diversity measures and their application to thinning

inferredcites →https://doi.org/10.1016/j.inffus.2006.10.002openalex-snapshot

Intrusion detection in computer networks by a modular ensemble of one-class classifiers

inferredcites →https://doi.org/10.1016/j.ins.2017.06.007openalex-snapshot

A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems

inferredcites →https://doi.org/10.1016/j.jksuci.2015.12.004openalex-snapshot

Intrusion detection model using fusion of chi-square feature selection and multi class SVM

inferredcites →https://doi.org/10.1016/j.jksuci.2018.03.011openalex-snapshot

Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms

inferredcites →https://doi.org/10.1016/j.jnca.2004.01.003openalex-snapshot

Intrusion detection using an ensemble of intelligent paradigms

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

Intrusion detection system: A comprehensive review

inferredcites →https://doi.org/10.1016/j.jnca.2016.03.011openalex-snapshot

Ensemble based collaborative and distributed intrusion detection systems: A survey

inferredcites →https://doi.org/10.1016/j.neucom.2012.01.048openalex-snapshot

Two-stage learning for multi-class classification using genetic programming

inferredcites →https://doi.org/10.1016/j.procs.2016.06.047openalex-snapshot

Random Forest Modeling for Network Intrusion Detection System

inferredcites →https://doi.org/10.1016/j.proeng.2012.01.827openalex-snapshot

A Hybrid Intelligent Approach for Network Intrusion Detection

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/comst.2015.2402161openalex-snapshot

Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset

inferredcites →https://doi.org/10.1109/greencom-ithings-cpscom.2013.122openalex-snapshot

Random Forest Classification for Detecting Android Malware

inferredcites →https://doi.org/10.1109/iceeot.2016.7755181openalex-snapshot

Class imbalance problem in the Network Intrusion Detection Systems

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Proceedings of the International Joint Conference on Neural Networks, 2003.

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Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005

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A Hybrid Genetic Programming and Boosting Technique for Learning Kernel Functions from Training Data

inferredcites →https://doi.org/10.1109/tc.2016.2519914openalex-snapshot

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

inferredcites →https://doi.org/10.1109/tcyb.2013.2247592openalex-snapshot

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

inferredcites →https://doi.org/10.1109/tcyb.2015.2404806openalex-snapshot

A Multiobjective Genetic Programming-Based Ensemble for Simultaneous Feature Selection and Classification

inferredcites →https://doi.org/10.1109/tcyb.2015.2423295openalex-snapshot

Binarization With Boosting and Oversampling for Multiclass Classification

inferredcites →https://doi.org/10.1109/tifs.2017.2762828openalex-snapshot

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

inferredcites →https://doi.org/10.1109/tkde.2008.181openalex-snapshot

Adapted One-versus-All Decision Trees for Data Stream Classification

inferredcites →https://doi.org/10.1109/tpami.2008.78openalex-snapshot

An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation

inferredcites →https://doi.org/10.1109/tsmcb.2007.914695openalex-snapshot

AdaBoost-Based Algorithm for Network Intrusion Detection

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

Random-Forests-Based Network Intrusion Detection Systems

inferredcites →https://doi.org/10.1109/wcnc.2017.7925567openalex-snapshot

IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach

inferredcites →https://doi.org/10.1145/1143844.1143921openalex-snapshot

Pruning in ordered bagging ensembles

inferredcites →https://doi.org/10.1145/1390156openalex-snapshot

Proceedings of the 25th international conference on Machine learning - ICML '08

inferredcites →https://doi.org/10.1145/846183.846200openalex-snapshot

Winning the KDD99 classification cup

inferredcites →https://doi.org/10.1155/2012/850160openalex-snapshot

The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

inferredcites →https://doi.org/10.2307/2531595openalex-snapshot

Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

inferredcites →https://doi.org/10.5120/13836-1402openalex-snapshot

An Ensemble Classification Approach for Intrusion Detection

inferredcites →https://doi.org/10.5120/15771-4100openalex-snapshot

An Efficient Algorithm for Network Intrusion Detection System

inferredcites →https://doi.org/10.5121/ijnsa.2010.2202openalex-snapshot

Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection

inferredcites →https://doi.org/10.5555/2969239.2969333openalex-snapshot

Precision-Recall-Gain Curves: PR Analysis Done Right

What points at this work

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

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Method for Out-of-Distribution Data Detection for AR Teaching Based on Semi-supervised Deep Learning Network

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A context-aware robust intrusion detection system: a reinforcement learning-based approach

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Smoke and mirrors: A variational optuna autoencoder with reinforcement learning for intrusion detection in IoT networks

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A Feature Reduced Intrusion Detection System with Optimized SVM Using Big Bang Big Crunch Optimization

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Pre-trained language model-enhanced conditional generative adversarial networks for intrusion detection

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Feature Extraction and Anomaly Detection Using Different Autoencoders for Modeling Intrusion Detection Systems

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LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system

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procmonML: Generating evasion resilient host-based behavioral analytics from tree ensembles

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EBDM: Ensemble binary detection models for multi-class wireless intrusion detection based on deep neural network

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Dynamic multi-scale topological representation for enhancing network intrusion detection

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M-AdaBoost-A based ensemble system for network intrusion detection

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Artificial intelligence for cybersecurity: Literature review and future research directions

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Improved intrusion detection method for communication networks using association rule mining and artificial neural networks

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Empirical Evaluation of Attacks Against IEEE 802.11 Enterprise Networks: The AWID3 Dataset

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Network Intrusion Detection with Two-Phased Hybrid Ensemble Learning and Automatic Feature Selection

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An Enhanced Genetic-based CIDE Framework for Scalable Anomaly Detection in Big Data

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Enhanced Intrusion Detection System with Modified Random Forest and Feature Selection on KDD

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Network Intrusion Detection on the IoT Edge Using Adversarial Autoencoders

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Enhancing Network Traffic Analysis with a Unique Imbalanced Data Algorithm in Deep Learning

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Efficient Anomaly Detection in Big Data using Improved Random Forest Model

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The Diminished Importance of Connection-based Features in Intrusion Detection

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The Untold Secrets of WiFi-Calling Services: Vulnerabilities, Attacks, and Countermeasures

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Network Traffic-Based Hybrid Malware Detection for Smartphone and Traditional Networked Systems

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A Survey on Data-driven Network Intrusion Detection

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Distance-based Probabilistic Data Augmentation for Synthetic Minority Oversampling

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Integration of Fuzzy with Incremental Import Vector Machine for Intrusion Detection

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Adversarial Sample Generation using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) and Classification for IEEE 802.11 using the Deep Deterministic Policy Gradient (DDPG)

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A cross-layer based optimized feature selection scheme for intrusion detection in wireless sensor network

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Classification Tendency Difference Index Model for Feature Selection and Extraction in Wireless Intrusion Detection

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Data-Driven Network Intrusion Detection: A Taxonomy of Challenges and Methods

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Peer Review #1 of "Network intrusion detection using oversampling technique and machine learning algorithms (v0.1)"

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Peer Review #2 of "Network intrusion detection using oversampling technique and machine learning algorithms (v0.1)"

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Peer Review #1 of "Network intrusion detection using oversampling technique and machine learning algorithms (v0.2)"

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Peer Review #2 of "Network intrusion detection using oversampling technique and machine learning algorithms (v0.2)"

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Network intrusion detection using oversampling technique and machine learning algorithms

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