Abstract
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-training (ST) model, and a modified version of self-training (MST) model. All the four approaches work in semi-supervised fashion, while the MST uses an ensemble of classifiers for the final decision making. SSML approaches are particularly useful when a limited number of labeled data is available for training and validation of the classification model. Manual labeling of a large dataset is complex and time consuming. It is even worse for the OBS network data. SSML can be used to leverage the unlabeled data for making a better prediction than using a smaller set of labelled data. We evaluated the performance of four SSML approaches for two (Behaving, Not-behaving), three (Behaving, Not-behaving, and Potentially Not-behaving), and four (No-Block, Block, NB- wait and NB-No-Block) class classifications using precision, recall, and F1 score. In case of the two-class classification, the K-means and GMM-based approaches performed better than the others. In case of the three-class classification, the K-means and the classical ST approaches performed better than the others. In case of the four-class classification, the MST showed the best performance. Finally, the SSML approaches were compared with two supervised learning (SL) based approaches. The comparison results showed that the SSML based approaches outperform when a smaller sized labeled data is available to train the classification models.
Highlights
Machine learning (ML) and data mining has been extensively used in communication networks for its ability to respond dynamically to the changes in networks without repetitive human intervention
This paper presents a comparative analysis of four semi supervised machine learning (SSML) approaches on an optical burst switching (OBS) network dataset for detecting a BHP flooding attack
We present a comparative analysis of these four SSML approaches on an OBS network dataset for detecting a BHP flooding attack
Summary
Machine learning (ML) and data mining has been extensively used in communication networks for its ability to respond dynamically to the changes in networks without repetitive human intervention. The authors suggested that using data mining algorithms, one can find the hidden relation or pattern of behavior related to network performance and network control parameters. The authors efficiently surveyed the existing literatures published in the period between 2017 and 2020 and listed the significant works on the application of machine learning in vulnerability prediction, routing, Quality of Service enhancement, intrusion detection, resource management, etc. This showed that machine learning models can be used to efficiently reduce the gap between the computational complexity of modern communication networks and their performance. In [3], Boutaba et al mentioned a wide-ranging applications of ML in communication networks
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