Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic

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Journal of Data Analysis and Information Processing

ISSN Print: 2327-7211
ISSN Online: 2327-7203
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"Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic"
written by Adel Ammar,
published by Journal of Data Analysis and Information Processing, Vol.3 No.2, 2015

has been cited by the following article(s):

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CrossRef

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[15]	A DISTANCE BASED ALGORITHM FOR NETWORK ANOMALY DETECTION USING INITIAL CLASSIFICATION OF 'PROTOCOL TYPE'ATTRIBUTE
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[16]	The Effect of Normalization on Intusion Detection Classifiers (Naïve Bayes and J48)
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[18]	The Effect of Normalization on Intrusion Detection Classifiers (Na ve Bayes and J48)
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[19]	A DISTANCE BASED ALGORITHM FOR NETWORK ANOMALY DETECTION USING INITIAL CLASSIFICATION OF'PROTOCOL TYPE'ATTRIBUTE.
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[20]	On Attack-Relevant Ranking of Network Features
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[1]	Internet of Things and Its Applications Lecture Notes in Electrical Engineering, 2022 DOI:10.1007/978-981-16-7637-6_35

[2]	A Hybrid Model for Anomaly-Based Intrusion Detection in Complex Computer Networks 2020 21st International Arab Conference on Information Technology (ACIT), 2020 DOI:10.1109/ACIT50332.2020.9299965

[3]	Impact of PDS Based kNN Classifiers on Kyoto Dataset International Journal of Rough Sets and Data Analysis, 2019 DOI:10.4018/IJRSDA.2019040105

[4]	A Clever Approach to Develop an Efficient Deep Neural Network Based IDS for Cloud Environments Using a Self-Adaptive Genetic Algorithm 2019 International Conference on Advanced Communication Technologies and Networking (CommNet), 2019 DOI:10.1109/COMMNET.2019.8742390

[5]	NSNAD: negative selection-based network anomaly detection approach with relevant feature subset Neural Computing and Applications, 2019 DOI:10.1007/s00521-019-04396-2

[6]	Progress in Advanced Computing and Intelligent Engineering Advances in Intelligent Systems and Computing, 2018 DOI:10.1007/978-981-10-6875-1_7

[7]	Data Fusion for Network Intrusion Detection: A Review Security and Communication Networks, 2018 DOI:10.1155/2018/8210614

Follow SCIRP

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