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.

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.

No abstract

Health is one of the basic needs of human life. If this need cannot be fulfilled, then nothing can compensate for the loss. Often in Bangladesh, we observe that the economic ability of mass people can hardly ensure proper health care. The poor are being deprived of proper health care and even though the rich class can afford quality health care, but it comes with many compromises with their valuable time and labor. To meet the gap, we thought of a smartphone-based health care system that will assist people, especially the underprivileged, to attain health care easily and effectively. To effectively use the ICT facility that the government and non-government sectors are providing, we made a mobile application that will have several features including online appointment and cabin booking, prescription generator, hospital, and doctor's information displayed along with the suggestion, medicinal and disease information, etc. Since there is a great need for a system that can alarm people of emerging diseases in their area, we made a system to alarm people about that. Moreover, in the world of continuous changes, there is a great need for communication to acquire the latest knowledge. Hence a social network where doctors can discuss cures and medication will be of great benefit. Along with this, the need for continuous observation of medicinal side effects is not negligible. So, we need a proper channel for communication between patient and doctor, especially when a patient encounters some trouble when he takes the pre-scribed medications because of their side effects.