An Integrated Intrusion Detection System by Combining SVM with AdaBoost

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ISSN Print: 1945-3116
ISSN Online: 1945-3124
www.scirp.org/journal/jsea
E-mail: jsea@scirp.org

"An Integrated Intrusion Detection System by Combining SVM with AdaBoost"
written by Yu Ren,
published by Journal of Software Engineering and Applications, Vol.7 No.12, 2014

has been cited by the following article(s):

[1]	Ultrasonic thyroid nodule detection method based on U-Net network
	2021

[2]	A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting
	2019

[3]	An Elaborate Comprehensive Survey on Recent Developments in Behaviour Based Intrusion Detection Systems
	2019

[4]	EXPANDING TREE-BASED CLASSIFIERS USING META-ALGORITHM APPROACH: AN APPLICATION FOR IDENTIFYING STUDENTS'COGNITIVE LEVEL
	International Journal of Innovative Computing, Information and Control, 2019

[5]	A Novel Intrusion Detection System based on IABRBFSVM for Wireless Sensor Networks
	2018

[6]	A LogitBoost-Based Algorithm for Detecting Known and Unknown Web Attacks
	2017

[1]	Ultrasonic thyroid nodule detection method based on U-Net network Computer Methods and Programs in Biomedicine, 2021 DOI:10.1016/j.cmpb.2020.105906

[2]	A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting Applied Artificial Intelligence, 2019 DOI:10.1080/08839514.2019.1582861

[3]	An Elaborate Comprehensive Survey on Recent Developments in Behaviour Based Intrusion Detection Systems 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 2019 DOI:10.1109/ICCIDS.2019.8862119

[4]	A Novel Intrusion Detection System based on IABRBFSVM for Wireless Sensor Networks Procedia Computer Science, 2018 DOI:10.1016/j.procs.2018.04.275

[5]	A LogitBoost-Based Algorithm for Detecting Known and Unknown Web Attacks IEEE Access, 2017 DOI:10.1109/ACCESS.2017.2766844

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