Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network

Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.

A Brief Review on the Methods that Improve Optical Burst Switching Network Performance

In this tutorial, we give an introduction to optical burst switching (OBS) networks and compare different methods that have been given to improve performance of these networks. OBS is an alternative to optical circuit switching and optical packet switching by separating data from control packets and sending them in a different wavelength. OBS networks suffer from an issue based on their bufferless nature, burst contentions. In these networks, when two data bursts are competing on the same wavelength, one of them is dropped and it leads to a significant reduction on the performance of the network. A lot of researches have been done to solve this problem. Some of them are addressed in this paper.