Adaptive intrusion detection via GA-GOGMM-based pattern learning with fuzzy rough set-based attribute selection-Reference-Cited by-同舟云学术

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Adaptive intrusion detection via GA-GOGMM-based pattern learning with fuzzy rough set-based attribute selection

Published:2020-01 Issue: Volume:139 Page:112845
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Liu Jinping^ORCID,Zhang Wuxia,Tang Zhaohui,Xie Yongfang,Ma Tianyu,Zhang Jingjing,Zhang Guoyong,Niyoyita Jean Paul

Funder

National Natural Science Foundation of China

Hunan Natural Science Fund

Huazhong University of Science and Technology

Hunan Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference48 articles.

1. Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system;Al-Yaseen;Expert Systems with Applications,2017

2. Learning Gaussian mixture models with entropy-based criteria;Antonio;IEEE Transactions on Neural Networks,2009

3. Using pattern-of-life as contextual information for anomaly-based intrusion detection systems;Aparicio-Navarro;IEEE Access : Practical Innovations, Open Solutions,2017

4. Fuzziness based semi-supervised learning approach for intrusion detection system;Ashfaq;Information Sciences,2017

5. Anomaly-based intrusion detection using extreme learning machine and aggregation of network traffic statistics in probability space;Atli;Cognitive Computation,2018

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