A Comparative Analysis of Semi-Supervised Learning in Detecting Burst Header Packet Flooding Attack in Optical Burst Switching Network

A Comparative Analysis of Semi-Supervised Learning in Detecting Burst Header Packet Flooding Attack in Optical Burst Switching Network

This paper presents a comparative analysis of four semi-supervised machine learning (SSML) algorithms for detecting malicious nodes in an optical burst switching (OBS) network. The SSML approaches include a modified version of K-means clustering, a Gaussian mixture model (GMM), a classical self-trai...

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Bibliographic Details
Main Authors: Md. Kamrul Hossain, Md. Mokammel Haque, M. Ali Akber Dewan
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Computers
Subjects:
optical burst switching network
burst header packet flooding attack
semi-supervised learning
self-training
clustering
Online Access:https://www.mdpi.com/2073-431X/10/8/95

Internet

https://www.mdpi.com/2073-431X/10/8/95

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