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

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

<p>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 perf...

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Bibliographic Details
Main Author: Zina Chkirbene (16869987) (author)
Other Authors: Aiman Erbad (14150589) (author), Ridha Hamila (7006457) (author), Amr Mohamed (3508121) (author), Mohsen Guizani (12580291) (author), Mounir Hamdi (14150652) (author)
Published: 2020
Subjects:
Information and computing sciences
Cybersecurity and privacy
Distributed computing and systems software
Machine learning
Feature extraction
Intrusion detection
Heuristic algorithms
Cloud computing
Computational modeling
Machine learning algorithms
Cloud security
Node past behavior
Feature selection
Trustworthiness
System cleansing
Machine learning techniques
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