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Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models Cover

Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

Cybernetics and Information Technologies
Volume 21 (2021): Issue 3 (September 2021)
By: Sumaiya Thaseen Ikram,  Aswani Kumar Cherukuri,  Babu Poorva,  Pamidi Sai Ushasree,  Yishuo Zhang,  Xiao Liu and  Gang Li  
Open Access
|Dec 2021

Authors

Sumaiya Thaseen Ikram

sumaiyathaseen@gmail.com

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Aswani Kumar Cherukuri

cherukuri@acm.org

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Babu Poorva

poorvababu@gmail.com

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Pamidi Sai Ushasree

ushasreepamidi1@gmail.com

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Yishuo Zhang

School of Information Technology, Deakin University, Australia

Xiao Liu

xiaou.liu@deakin.edu.au

School of Information Technology, Deakin University, Australia

Gang Li

gang.li@deakin.edu.au

School of Information Technology, Deakin University, Australia
DOI: https://doi.org/10.2478/cait-2021-0037 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
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Language: English
Page range: 175 - 188
Submitted on: Jul 29, 2021
|
Accepted on: Sep 3, 2021
|
Published on: Dec 7, 2021
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Accuracy,
Backpropagation network,
Intrusion detection,
Multilayer perceptron,
Long short term memory
Related subjects:
Computer sciences,
Information technology

© 2021 Sumaiya Thaseen Ikram, Aswani Kumar Cherukuri, Babu Poorva, Pamidi Sai Ushasree, Yishuo Zhang, Xiao Liu, Gang Li, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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