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A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest

https://doi.org/10.3390/electronics9040577

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What this work says

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

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Multi-classification of UNSW-NB15 Dataset for Network Anomaly Detection System

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Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems

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An IWD-based feature selection method for intrusion detection system

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A hybrid method consisting of GA and SVM for intrusion detection system

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A feature selection approach to find optimal feature subsets for the network intrusion detection system

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A hybrid network intrusion detection system using simplified swarm optimization (SSO)

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A survey on feature selection methods

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

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Firefly algorithm based feature selection for network intrusion detection

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Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications

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A feature reduced intrusion detection system using ANN classifier

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Fuzziness based semi-supervised learning approach for intrusion detection system

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Improving network intrusion detection system performance through quality of service configuration and parallel technology

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Cyber intrusion detection by combined feature selection algorithm

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

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Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

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

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A fast and elitist multiobjective genetic algorithm: NSGA-II

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Robust Massively Parallel Dynamic State Estimation of Power Systems Against Cyber-Attack

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TSDL: A Two-Stage Deep Learning Model for Efficient Network Intrusion Detection

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A detailed analysis of the KDD CUP 99 data set

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Accelerated deep neural networks for enhanced Intrusion Detection System

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)

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An Intrusion-Detection Model

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Combining labeled and unlabeled data with co-training

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A Two-Stage Classifier Approach using RepTree Algorithm for Network Intrusion Detection

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Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

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Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

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A Deep Learning Approach for Network Intrusion Detection System

What points at this work

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

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The Multi-objective Feature Selection in Android Malware Detection System

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Evaluation Metrics for a Hybrid Classification System Based on the Distributivity Equation and the UNSW-NB15 Cyberattack Dataset

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Network Intrusion Detection System Using Gradient Boosting Algorithm

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Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification

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Effectiveness of an ensemble technique based on the distributivity equation in detecting suspicious network activity

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RETRACTED: An intrusion identification and prevention for cloud computing: From the perspective of deep learning

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