A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning

Digital assets have come under various network security threats in the digital age. As a kind of security equipment to protect digital assets, intrusion detection system (IDS) is less efficient if the alert is not timely and IDS is useless if the accuracy cannot meet the requirements. Therefore, an...

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Vydané v:	IEEE access Ročník 9; s. 75729 - 75740
Hlavní autori:	Liu, Chao, Gu, Zhaojun, Wang, Jialiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Adaptive sampling Algorithms Artificial neural networks Classification Classification algorithms Computer networks Datasets Deep learning deep learning algorithm Distributed processing Feature extraction Hybrid systems Intrusion detection Intrusion detection system Intrusion detection systems k-means Machine learning machine learning algorithm Machine learning algorithms Radio frequency random forest Security Sparks
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
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