A K-Means Clustering and SVM based Hybrid Concept Drift Detection Technique for Network Anomaly Detection

Expert Systems with Applications ◽

10.1016/j.eswa.2022.116510 ◽

2022 ◽

pp. 116510

Author(s):

Meenal Jain ◽

Gagandeep Kaur ◽

Vikas Saxena

Keyword(s):

Anomaly Detection ◽

Concept Drift ◽

Detection Technique ◽

Hybrid Concept ◽

Concept Drift Detection ◽

Network Anomaly Detection

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Network anomaly detection technique based on LSTM network and UNSW-NB15 dataset

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/340942020 ◽

2020 ◽

Vol 9 (4) ◽

pp. 6527-6532

Author(s):

Lai Van Duong

Keyword(s):

Anomaly Detection ◽

Detection Technique ◽

Lstm Network ◽

Network Anomaly Detection

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A New Network Anomaly Detection Technique Based on Per-Flow and Per-Service Statistics

Computational Intelligence and Security - Lecture Notes in Computer Science ◽

10.1007/11596981_37 ◽

2005 ◽

pp. 252-259 ◽

Author(s):

Yuji Waizumi ◽

Daisuke Kudo ◽

Nei Kato ◽

Yoshiaki Nemoto

Keyword(s):

Anomaly Detection ◽

Detection Technique ◽

Network Anomaly Detection

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Distributed anomaly detection using concept drift detection based hybrid ensemble techniques in streamed network data

Cluster Computing ◽

10.1007/s10586-021-03249-9 ◽

2021 ◽

Author(s):

Meenal Jain ◽

Gagandeep Kaur

Keyword(s):

Anomaly Detection ◽

Concept Drift ◽

Network Data ◽

Ensemble Techniques ◽

Concept Drift Detection

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A lightweight network anomaly detection technique

2017 International Conference on Computing, Networking and Communications (ICNC) ◽

10.1109/iccnc.2017.7876251 ◽

2017 ◽

Author(s):

Jinoh Kim ◽

Wucherl Yoo ◽

Alex Sim ◽

Sang C. Suh ◽

Ikkyun Kim

Keyword(s):

Anomaly Detection ◽

Detection Technique ◽

Network Anomaly Detection

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Testing Concept Drift Detection Technique on Data Stream

Big Data Analytics - Lecture Notes in Computer Science ◽

10.1007/978-3-030-04780-1_6 ◽

2018 ◽

pp. 89-99

Author(s):

Narinder Singh Punn ◽

Sonali Agarwal

Keyword(s):

Data Stream ◽

Concept Drift ◽

Detection Technique ◽

Concept Drift Detection

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Classification method for data stream based on concept drift detection technique

2015 4th International Conference on Computer Science and Network Technology (ICCSNT) ◽

10.1109/iccsnt.2015.7490826 ◽

2015 ◽

Author(s):

Wang Jianhua ◽

Li Xiaofeng ◽

Gao Weiwei

Keyword(s):

Data Stream ◽

Concept Drift ◽

Detection Technique ◽

Classification Method ◽

Concept Drift Detection

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An Effective Concept Drift Detection Technique with Kernel Extreme Learning Machine for Email Spam Filtering

2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) ◽

10.1109/iciss49785.2020.9316055 ◽

2020 ◽

Author(s):

S Priya ◽

R Annie Uthra

Keyword(s):

Extreme Learning Machine ◽

Concept Drift ◽

Detection Technique ◽

Spam Filtering ◽

Kernel Extreme Learning Machine ◽

Concept Drift Detection ◽

Learning Machine ◽

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Improved negative selection algorithm for network anomaly detection on high-dimensional data

Journal of Computer Applications ◽

10.3724/sp.j.1087.2009.00805 ◽

2009 ◽

Vol 29 (3) ◽

pp. 805-807 ◽

Author(s):

Wen-zhong GUO ◽

Guo-long CHEN ◽

Qing-liang CHEN

Keyword(s):

Anomaly Detection ◽

Negative Selection ◽

High Dimensional Data ◽

High Dimensional ◽

Selection Algorithm ◽

Negative Selection Algorithm ◽

Network Anomaly Detection

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Network anomaly detection based on selective ensemble algorithm

The Journal of Supercomputing ◽

10.1007/s11227-020-03374-z ◽

2020 ◽

Author(s):

Hongle Du ◽

Yan Zhang

Keyword(s):

Anomaly Detection ◽

Selective Ensemble ◽

Ensemble Algorithm ◽

Network Anomaly Detection

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Concept drift detection and localization in process mining

Proceedings of the 36th Annual ACM Symposium on Applied Computing ◽

10.1145/3412841.3441918 ◽

2021 ◽

Author(s):

Rafael Gaspar de Sousa ◽

Sarajane Marques Peres ◽

Marcelo Fantinato ◽

Hajo Alexander Reijers

Keyword(s):

Process Mining ◽

Concept Drift ◽

Concept Drift Detection ◽

Detection And Localization

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