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Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach

https://doi.org/10.1109/access.2019.2929487

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What this work says

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

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Estimating attributes: Analysis and extensions of RELIEF

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Measuring Diversity and Accuracy in ANN Ensembles

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Big Data Analytics for Intrusion Detection System: Statistical Decision-Making Using Finite Dirichlet Mixture Models

inferredcites →https://doi.org/10.1007/978-3-319-99843-5_11openalex-snapshot

Analysis of Cybersecurity Threats in Industry 4.0: The Case of Intrusion Detection

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Hypothesis Testing and ANOVA

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High Speed Network Intrusion Detection System Using FPGA

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The use of computational intelligence in intrusion detection systems: A review

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

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Swarm intelligence in intrusion detection: A survey

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

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Network intrusion detection system based on recursive feature addition and bigram technique

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Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection

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

Firefly algorithm based feature selection for network intrusion detection

inferredcites →https://doi.org/10.1016/j.cose.2019.02.008openalex-snapshot

CorrCorr: A feature selection method for multivariate correlation network anomaly detection techniques

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An empirical examination of the influence of biased personalized product recommendations on consumers' decision making outcomes

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A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems

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A feature reduced intrusion detection system using ANN classifier

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Designing collaborative blockchained signature-based intrusion detection in IoT environments

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On developing an automatic threshold applied to feature selection ensembles

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Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs

inferredcites →https://doi.org/10.1016/j.jisa.2018.11.007openalex-snapshot

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A holistic review of Network Anomaly Detection Systems: A comprehensive survey

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An improved NSGA-III algorithm for feature selection used in intrusion detection

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An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization

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Feature selection in machine learning: A new perspective

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The Impact of Internet-Technologies Development on Small Business Success in Russia

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Combination of Similarity Rankings Using Data Fusion

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Induction of Decision Trees

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Evaluating presence–absence models in ecology: the need to account for prevalence

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Constructing important features from massive network traffic for lightweight intrusion detection

inferredcites →https://doi.org/10.1088/1742-6596/1192/1/012018openalex-snapshot

Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset

inferredcites →https://doi.org/10.1088/1742-6596/887/1/012031openalex-snapshot

Hybrid feature selection for supporting lightweight intrusion detection systems

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Robust rank aggregation for gene list integration and meta-analysis

inferredcites →https://doi.org/10.1109/access.2017.2780250openalex-snapshot

HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection

inferredcites →https://doi.org/10.1109/access.2018.2863036openalex-snapshot

Enhanced Network Anomaly Detection Based on Deep Neural Networks

inferredcites →https://doi.org/10.1109/access.2018.2875045openalex-snapshot

Distributed Abnormal Behavior Detection Approach Based on Deep Belief Network and Ensemble SVM Using Spark

inferredcites →https://doi.org/10.1109/access.2018.2883142openalex-snapshot

Building a Cloud IDS Using an Efficient Feature Selection Method and SVM

inferredcites →https://doi.org/10.1109/access.2019.2895334openalex-snapshot

Deep Learning Approach for Intelligent Intrusion Detection System

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/comst.2014.2336610openalex-snapshot

A Survey of Distance and Similarity Measures Used Within Network Intrusion Anomaly Detection

inferredcites →https://doi.org/10.1109/comst.2015.2494502openalex-snapshot

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

inferredcites →https://doi.org/10.1109/icissec.2016.7885840openalex-snapshot

An Evaluation Framework for Intrusion Detection Dataset

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/milcis.2015.7348942openalex-snapshot

UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

inferredcites →https://doi.org/10.1109/tbdata.2017.2715166openalex-snapshot

Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks

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/tsg.2016.2614603openalex-snapshot

Modeling of Intrusion and Defense for Assessment of Cyber Security at Power Substations

inferredcites →https://doi.org/10.1145/382912.382923openalex-snapshot

Testing Intrusion detection systems

inferredcites →https://doi.org/10.1186/s13638-016-0623-3openalex-snapshot

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

inferredcites →https://doi.org/10.1201/9780429020247openalex-snapshot

Emerging Internet-Based Technologies

inferredcites →https://doi.org/10.3390/app8091535openalex-snapshot

A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection

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.

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Peer Review #1 of "Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation (v0.1)"

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Peer Review #2 of "Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation (v0.1)"

inferred← citeshttps://doi.org/10.7717/peerj-cs.327openalex-snapshot

Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation

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