CICIDS-2017 dataset feature analysis with information gain for anomaly detection

CICIDS-2017 dataset feature analysis with information gain for anomaly detection

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze...

Full description

Saved in:
Bibliographic Details
Main Authors: Kurniabudi, Kurniabudi, Stiawan, Deris, Darmawijoyo, Darmawijoyo, Idris, Mohd. Yazid, Bamhdi, Alwi M., Budiarto, Rahmat
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/93850/1/CICIDS-MohdYazidIdris2020_CICIDS2017DatasetFeatureAnalysisWithInformation.pdf
http://eprints.utm.my/id/eprint/93850/
http://dx.doi.org/10.1109/ACCESS.2020.3009843
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Malaysia
Language: English

Internet

http://eprints.utm.my/id/eprint/93850/1/CICIDS-MohdYazidIdris2020_CICIDS2017DatasetFeatureAnalysisWithInformation.pdf
http://eprints.utm.my/id/eprint/93850/
http://dx.doi.org/10.1109/ACCESS.2020.3009843

Similar Items