TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection

TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection

Machine learning techniques are becoming mainstream in intrusion detection systems as they allow real-time response and have the ability to learn and adapt. By using a comprehensive dataset with multiple attack types, a well-trained model can be created to improve the anomaly detection performance....

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Bibliographic Details
Main Authors: Zina Chkirbene, Aiman Erbad, Ridha Hamila, Amr Mohamed, Mohsen Guizani, Mounir Hamdi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Cloud security
node past behavior
feature selection
trustworthiness
system cleansing
machine learning techniques
Online Access:https://ieeexplore.ieee.org/document/9094213/

Internet

https://ieeexplore.ieee.org/document/9094213/

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