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Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking
Seyed Mohammad Hadi Mirsadeghi
,
Hayretdin Bahsi
, Risto Vaarandi
, Wissem Inoubli
Research output
:
Contribution to journal
›
Article
›
peer-review
20
Scopus citations
Overview
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Dive into the research topics of 'Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking'. Together they form a unique fingerprint.
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Computer Science
Software Defined Networking
100%
Software-Defined Networking
100%
Class Imbalance Problem
100%
Siamese Neural Network
16%
One-shot Learning
16%
network intrusion detection
16%
Level Technique
16%
Class Imbalance
16%
Class Instance
16%
Keyphrases
Class Imbalance Problem
100%
Random Undersampling
28%
Siamese
14%
Network Intrusion Detection
14%
RF Performance
14%
Method of Levels
14%
False Negative Rate
14%
Balancing Techniques
14%
Intrusion Datasets
14%
Class Imbalance
14%
Imbalanced Learning
14%
Imbalance Problem
14%
Siamese Neural Network
14%
One-shot Learning
14%
Generative Modeling
14%
GAN Neural Network
14%
Weighted Random Forest
14%