Category-Based Intrusion Detection Using PCA

Journal of Information Security

ISSN Print: 2153-1234
ISSN Online: 2153-1242
www.scirp.org/journal/jis
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Google-based Impact Factor: 3.25
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"Category-Based Intrusion Detection Using PCA"
written by Gholam Reza Zargar, Tania Baghaie,
published by Journal of Information Security, Vol.3 No.4, 2012

has been cited by the following article(s):

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[13]	A Proposed Model for Dimensionality Reduction to Improve the Classification Capability of Intrusion Protection Systems
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[14]	An empirical model in intrusion detection systems using principal component analysis and deep learning models
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[15]	Feature Selection Ranking and Subset-Based Techniques with Different Classifiers for Intrusion Detection
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[18]	Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset
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[19]	A Proposed Machine Learning based Scheme for Intrusion Detection
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[20]	Intrusion Detection System Using Data Mining
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[21]	A PCA-based method for IoT network traffic anomaly detection
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[22]	Efficient Techniques for Attack Detection Using Different Features Selection Algorithms and Classifiers
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[23]	Intrusion detection in RFID system using computational intelligence approach for underground mines
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[24]	Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic.
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[25]	Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques
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[26]	Research on Translation and Rotation in Handwritten Digits Recognition
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[34]	Study of stochastic and machine learning tecniques for anomaly-based Web atack detection
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[36]	Feature Based Unsupervised Intrusion Detection
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[40]	Intrusion Detection System Using Data Mining Technique: Support Vector Machine
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[41]	Intrusion Detection System Based on K-Star Classifier and Feature Set Reduction
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[1]	A Performance Analysis of Machine Learning Models for Attack Prediction using Different Feature Selection Techniques 2022 IEEE/ACIS 7th International Conference on Big Data, Cloud Computing, and Data Science (BCD), 2022 DOI:10.1109/BCD54882.2022.9900597

[2]	A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies Entropy, 2021 DOI:10.3390/e23050529

[3]	Detecting Anomalous Network Traffic in IoT Networks 2019 21st International Conference on Advanced Communication Technology (ICACT), 2019 DOI:10.23919/ICACT.2019.8702032

[4]	Feature Selection Ranking and Subset-Based Techniques with Different Classifiers for Intrusion Detection Wireless Personal Communications, 2019 DOI:10.1007/s11277-019-06864-3

[5]	Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks IEEE Transactions on Big Data, 2019 DOI:10.1109/TBDATA.2017.2715166

[6]	A Proposed Machine Learning based Scheme for Intrusion Detection 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018 DOI:10.1109/ICECA.2018.8474803

[7]	A PCA-based method for IoT network traffic anomaly detection 2018 20th International Conference on Advanced Communication Technology (ICACT), 2018 DOI:10.23919/ICACT.2018.8323766

[8]	Intrusion detection in RFID system using computational intelligence approach for underground mines International Journal of Communication Systems, 2018 DOI:10.1002/dac.3532

[9]	Efficient Techniques for Attack Detection Using Different Features Selection Algorithms and Classifiers Wireless Personal Communications, 2018 DOI:10.1007/s11277-018-5662-0

[10]	Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset Cybernetics and Information Technologies, 2018 DOI:10.2478/cait-2018-0042

[11]	Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset Cybernetics and Information Technologies, 2018 DOI:10.2478/cait-2018-0042

[12]	Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset Cybernetics and Information Technologies, 2018 DOI:10.2478/cait-2018-0042

[13]	Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset Cybernetics and Information Technologies, 2018 DOI:10.2478/cait-2018-0042

[14]	Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset Cybernetics and Information Technologies, 2018 DOI:10.2478/cait-2018-0042

[15]	Research on Translation and Rotation in Handwritten Digits Recognition 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), 2017 DOI:10.1109/ICCTEC.2017.00240

[16]	Network intrusion detection system using J48 Decision Tree 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015 DOI:10.1109/ICACCI.2015.7275914

[17]	Network backbone anomaly detection using double random forests based on non-extensive entropy feature extraction 2013 Ninth International Conference on Natural Computation (ICNC), 2013 DOI:10.1109/ICNC.2013.6817948

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