Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection

Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection

Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network activity and to protect systems and the Internet of Things (IoT) devices from attacks (or intrusions). The problem with these systems is that they generate a huge amount of inappropriate false alarms whenever abno...

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Bibliographic Details
Main Authors: Wajdi Alhakami, Abdullah ALharbi, Sami Bourouis, Roobaea Alroobaea, Nizar Bouguila
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Intrusion detection systems (IDS)
anomaly intrusion detection
infinite mixture models
bounded generalized Gaussian models
Bayesian inference
Markov chain Monte Carlo (MCMC)
Online Access:https://ieeexplore.ieee.org/document/8693712/

Internet

https://ieeexplore.ieee.org/document/8693712/

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