scholarly journals Towards Model Generalization for Intrusion Detection: Unsupervised Machine Learning Techniques

2021 ◽  
Vol 30 (1) ◽  
Author(s):  
Miel Verkerken ◽  
Laurens D’hooge ◽  
Tim Wauters ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Model Generalization ◽  
Learning Techniques

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

In-Network Data Aggregation for Information-Centric WSNs using Unsupervised Machine Learning Techniques

2021 ◽  
Author(s):  
Marcelo E. Pellenz ◽  
Rosana Lachowski ◽  
Edgard Jamhour ◽  
Glauber Brante ◽  
Guilherme Luiz Moritz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Aggregation ◽  
Machine Learning Techniques ◽  
Network Data ◽  
Unsupervised Machine Learning ◽  
Learning Techniques
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