"Feature Selection for Intrusion Detection Using Random Forest"
written by Md. Al Mehedi Hasan, Mohammed Nasser, Shamim Ahmad, Khademul Islam Molla,
published by Journal of Information Security, Vol.7 No.3, 2016
has been cited by the following article(s):
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[18] Survey on SDN based network intrusion detection system using machine learning approaches
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[19] Recent Advances in Ensembles for Feature Selection
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[20] Random Forest Algorithm in Intrusion Detection System: A Survey
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[21] Automatic Intrusion Detection System Using Deep Recurrent Neural Network Paradigm
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[22] Emerging Challenges
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[23] Network Data Security for the Detection System in the Internet of Things with Deep Learning Approach
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[24] On Evaluation of Network Intrusion Detection Systems: Statistical Analysis of CIDDS-001 Dataset Using Machine Learning Techniques.
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[25] A feature selection algorithm for IDS
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[26] A Hybrid Feature Selection Method for Improved Detection of Wired/Wireless Network Intrusions
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[29] Deep Learning Approach for Intrusion Detection System (IDS) in the Internet of Things (IoT) Network using Gated Recurrent Neural Networks (GRU)
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[30] A Review on Cyber Security Datasets for Machine Learning Algorithms
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[31] DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data
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[32] A Hybrid Feature Selection Framework for Enhancing Network Intrusion Detection
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