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Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

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inferredcites →https://doi.org/10.1007/978-0-387-30164-8openalex-snapshot
Encyclopedia of Machine Learning
inferredcites →https://doi.org/10.1007/978-3-030-20257-6openalex-snapshot
Engineering Applications of Neural Networks
inferredcites →https://doi.org/10.1007/978-3-319-11698-3openalex-snapshot
Network and System Security
inferredcites →https://doi.org/10.1007/978-3-540-76725-1_44openalex-snapshot
Conditional Mutual Information Based Feature Selection for Classification Task
inferredcites →https://doi.org/10.1007/s00521-021-06093-5openalex-snapshot
An effective NIDS framework based on a comprehensive survey of feature optimization and classification techniques
inferredcites →https://doi.org/10.1007/s10207-019-00434-1openalex-snapshot
DDoS attack detection with feature engineering and machine learning: the framework and performance evaluation
inferredcites →https://doi.org/10.1007/s10207-020-00508-5openalex-snapshot
A novel scalable intrusion detection system based on deep learning
inferredcites →https://doi.org/10.1007/s10207-021-00567-2openalex-snapshot
A content-based deep intrusion detection system
inferredcites →https://doi.org/10.1007/s10255-019-0856-0openalex-snapshot
An Inexact Affine Scaling Levenberg-Marquardt Method Under Local Error Bound Conditions
inferredcites →https://doi.org/10.1007/s10462-021-10037-9openalex-snapshot
A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
inferredcites →https://doi.org/10.1007/s10489-018-1141-2openalex-snapshot
Semi-supervised machine learning approach for DDoS detection
inferredcites →https://doi.org/10.1007/s12652-020-02167-9openalex-snapshot
Attack classification using feature selection techniques: a comparative study
inferredcites →https://doi.org/10.1007/s13042-018-00906-1openalex-snapshot
Review: machine learning techniques applied to cybersecurity
inferredcites →https://doi.org/10.1007/s13369-019-03970-zopenalex-snapshot
Analysis of Support Vector Machine-based Intrusion Detection Techniques
inferredcites →https://doi.org/10.1016/j.comnet.2017.08.013openalex-snapshot
Toward a reliable anomaly-based intrusion detection in real-world environments
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.cose.2019.06.005openalex-snapshot
A survey of network-based intrusion detection data sets
inferredcites →https://doi.org/10.1016/j.eswa.2015.07.015openalex-snapshot
An intrusion detection system using network traffic profiling and online sequential extreme learning machine
inferredcites →https://doi.org/10.1016/j.heliyon.2018.e00938openalex-snapshot
State-of-the-art in artificial neural network applications: A survey
inferredcites →https://doi.org/10.1016/j.jisa.2018.11.007openalex-snapshot
Cyber intrusion detection by combined feature selection algorithm
inferredcites →https://doi.org/10.1016/j.jisa.2022.103341openalex-snapshot
Adversarial machine learning in IoT from an insider point of view
inferredcites →https://doi.org/10.1016/j.jnca.2011.01.002openalex-snapshot
Mutual information-based feature selection for intrusion detection systems
inferredcites →https://doi.org/10.1016/j.jocs.2017.03.006openalex-snapshot
Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model
inferredcites →https://doi.org/10.1016/j.jss.2019.01.051openalex-snapshot
Architectural Tactics for Big Data Cybersecurity Analytics Systems: A Review
inferredcites →https://doi.org/10.1016/j.neucom.2017.11.077openalex-snapshot
Feature selection in machine learning: A new perspective
inferredcites →https://doi.org/10.1016/j.patcog.2019.02.023openalex-snapshot
The impact of class imbalance in classification performance metrics based on the binary confusion matrix
inferredcites →https://doi.org/10.1016/j.procs.2020.03.330openalex-snapshot
A Review of the Advancement in Intrusion Detection Datasets
inferredcites →https://doi.org/10.1016/j.procs.2020.04.133openalex-snapshot
Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review
inferredcites →https://doi.org/10.1016/j.procs.2021.06.066openalex-snapshot
Review the performance of the Bernoulli Naïve Bayes Classifier in Intrusion Detection Systems using Recursive Feature Elimination with Cross-validated selection of the best number of features
inferredcites →https://doi.org/10.1016/j.proeng.2011.11.2647openalex-snapshot
Overview of Support Vector Machine in Modeling Machining Performances
inferredcites →https://doi.org/10.1016/j.promfg.2019.06.011openalex-snapshot
Prediction performance of improved decision tree-based algorithms: a review
inferredcites →https://doi.org/10.1016/j.protcy.2013.12.159openalex-snapshot
The Effect of Data Pre-processing on Optimized Training of Artificial Neural Networks
inferredcites →https://doi.org/10.1038/srep10981openalex-snapshot
Mutual Information between Discrete Variables with Many Categories using Recursive Adaptive Partitioning
inferredcites →https://doi.org/10.1080/23742917.2019.1623475openalex-snapshot
Ensemble-based semi-supervised learning approach for a distributed intrusion detection system
inferredcites →https://doi.org/10.1088/1742-6596/1192/1/012068openalex-snapshot
Overview Feature Selection using Fish Swarm Algorithm
inferredcites →https://doi.org/10.1088/1757-899x/1013/1/012038openalex-snapshot
Comparative analysis of Machine Learning algorithms for Intrusion Detection
inferredcites →https://doi.org/10.1109/34.990133openalex-snapshot
Unsupervised feature selection using feature similarity
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/72.298224openalex-snapshot
Using mutual information for selecting features in supervised neural net learning
inferredcites →https://doi.org/10.1109/72.977291openalex-snapshot
Input feature selection for classification problems
inferredcites →https://doi.org/10.1109/access.2019.2893871openalex-snapshot
Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems
inferredcites →https://doi.org/10.1109/acit50332.2020.9299965openalex-snapshot
A Hybrid Model for Anomaly-Based Intrusion Detection in Complex Computer Networks
inferredcites →https://doi.org/10.1109/bigdata.2017.8258163openalex-snapshot
A filter-based feature selection model for anomaly-based intrusion detection systems
inferredcites →https://doi.org/10.1109/bigdataservice.2018.00050openalex-snapshot
Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm
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/confluence.2018.8442693openalex-snapshot
An Ensemble Approach for Intrusion Detection System Using Machine Learning Algorithms
inferredcites →https://doi.org/10.1109/cqr47547.2020.9101394openalex-snapshot
Data Processing and Model Selection for Machine Learning-based Network Intrusion Detection
inferredcites →https://doi.org/10.1109/i-smac.2018.8653735openalex-snapshot
Anomaly Based Novel Intrusion Detection System For Network Traffic Reduction
inferredcites →https://doi.org/10.1109/icacci.2014.6968253openalex-snapshot
An efficient task scheduling algorithm for heterogeneous multi-cloud environment
inferredcites →https://doi.org/10.1109/icca51439.2020.9264568openalex-snapshot
Intrusion Detection Using Principal Component Analysis and Support Vector Machines
inferredcites →https://doi.org/10.1109/iccsp48568.2020.9182197openalex-snapshot
Machine Learning Mechanisms for Network Anomaly Detection System: A Review
inferredcites →https://doi.org/10.1109/icoei.2019.8862784openalex-snapshot
Machine Learning Based Intrusion Detection System
inferredcites →https://doi.org/10.1109/ics51289.2020.00088openalex-snapshot
Feature Selection for Malicious Traffic Detection with Machine Learning
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/tc.2016.2519914openalex-snapshot
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm
inferredcites →https://doi.org/10.1109/tcyb.2020.3036393openalex-snapshot
Affine Transformation-Enhanced Multifactorial Optimization for Heterogeneous Problems
inferredcites →https://doi.org/10.1109/tencon.2019.8929510openalex-snapshot
An Intrusion Detection System Using Unsupervised Feature Selection
inferredcites →https://doi.org/10.1109/tla.2021.9448311openalex-snapshot
Empirical Exploration of Machine Learning Techniques for Detection of Anomalies Based on NIDS
inferredcites →https://doi.org/10.1109/tnn.2010.2047114openalex-snapshot
Discriminative Semi-Supervised Feature Selection Via Manifold Regularization
inferredcites →https://doi.org/10.1109/tpami.2005.159openalex-snapshot
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
inferredcites →https://doi.org/10.1137/1.9781611972771.75openalex-snapshot
Semi-supervised Feature Selection via Spectral Analysis
inferredcites →https://doi.org/10.1142/s0218539316500017openalex-snapshot
Evaluating Feature Selection Methods for Network Intrusion Detection with Kyoto Data
inferredcites →https://doi.org/10.1145/1273496.1273600openalex-snapshot
Supervised feature selection via dependence estimation
inferredcites →https://doi.org/10.1145/1978672.1978676openalex-snapshot
Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation
inferredcites →https://doi.org/10.1145/3424311.3424330openalex-snapshot
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection
inferredcites →https://doi.org/10.1186/1687-1499-2013-271openalex-snapshot
Intelligent feature selection and classification techniques for intrusion detection in networks: a survey
inferredcites →https://doi.org/10.1186/s40537-020-00382-xopenalex-snapshot
A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data
inferredcites →https://doi.org/10.1186/s42400-019-0038-7openalex-snapshot
Survey of intrusion detection systems: techniques, datasets and challenges
inferredcites →https://doi.org/10.1213/ane.0000000000002864openalex-snapshot
Correlation Coefficients: Appropriate Use and Interpretation
inferredcites →https://doi.org/10.13140/rg.2.2.35333.40168openalex-snapshot
The Bro Intrusion Detection System
inferredcites →https://doi.org/10.1504/ijdats.2018.094132openalex-snapshot
Multi-criteria decision support for feature selection in network anomaly detection system
inferredcites →https://doi.org/10.2298/fuee1902315oopenalex-snapshot
Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network
inferredcites →https://doi.org/10.2298/fuee2202269popenalex-snapshot
Wk-fnn design for detection of anomalies in the computer network traffic
inferredcites →https://doi.org/10.26417/ejef.v4i1.p7-13openalex-snapshot
Detection of Anomalies in the Computer Network Behaviour
inferredcites →https://doi.org/10.32604/csse.2022.021217openalex-snapshot
Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection
inferredcites →https://doi.org/10.32604/csse.2023.026712openalex-snapshot
Anomaly Detection for Industrial Internet of Things Cyberattacks
inferredcites →https://doi.org/10.3390/app11177868openalex-snapshot
Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
inferredcites →https://doi.org/10.3390/app8091535openalex-snapshot
A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection
inferredcites →https://doi.org/10.3390/electronics11223718openalex-snapshot
Cybersecurity in Smart Cities: Detection of Opposing Decisions on Anomalies in the Computer Network Behavior
inferredcites →https://doi.org/10.3390/electronics9122114openalex-snapshot
Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System
inferredcites →https://doi.org/10.3390/sym12061046openalex-snapshot
A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms
inferredcites →https://doi.org/10.35940/ijitee.i8469.0881019openalex-snapshot
Building an Effective Intrusion Detection System using combined Signature and Anomaly Detection Techniques
inferredcites →https://doi.org/10.3837/tiis.2018.10.024openalex-snapshot
Feature Selection Algorithms in Intrusion Detection System: A Survey
inferredcites →https://doi.org/10.3934/bioeng.2017.1.179openalex-snapshot
Dimension reduction methods for microarray data: a review
inferredcites →https://doi.org/10.4018/ijaec.2016100102openalex-snapshot
Review on Feature Selection and Classification using Neuro-Fuzzy Approaches
inferredcites →https://doi.org/10.4236/jis.2015.63025openalex-snapshot
Evaluation of Modified Vector Space Representation Using ADFA-LD and ADFA-WD Datasets
inferredcites →https://doi.org/10.5121/ijnsa.2020.12101openalex-snapshot
A Survey on the Use of Data Clustering for Intrusion Detection System in Cybersecurity
inferredcites →https://doi.org/10.5220/0006639801080116openalex-snapshot
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
inferredcites →https://doi.org/10.5539/cis.v11n3p67openalex-snapshot
Machine Learning Approach to Combat False Alarms in Wireless Intrusion Detection System
inferredcites →https://doi.org/10.5555/1005332.1016787openalex-snapshot
Feature Selection for Unsupervised Learning
inferredcites →https://doi.org/10.5937/vojtehg66-16670openalex-snapshot
Review of KDD Cup '99, NSL-KDD and Kyoto 2006+ datasets

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