ORCID Profile
0000-0003-3396-6568
Current Organisation
Chittagong University of Engineering and Technology
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Publisher: Korean Society for Internet Information (KSII)
Date: 25-10-2008
Publisher: Springer Singapore
Date: 2020
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 02-2008
Publisher: Institute of Advanced Engineering and Science
Date: 08-2020
DOI: 10.11591/IJECE.V10I4.PP4340-4351
Abstract: 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.
Publisher: Institution of Engineering and Technology (IET)
Date: 03-2017
Publisher: Springer Nature Singapore
Date: 30-09-2022
Publisher: Springer Singapore
Date: 2021
Publisher: IEEE
Date: 11-2015
Publisher: Institution of Engineering and Technology (IET)
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 12-2014
Publisher: Institute of Advanced Engineering and Science
Date: 10-2019
DOI: 10.11591/IJECE.V9I5
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-04-2018
Publisher: IEEE
Date: 02-2017
Publisher: MECS Publisher
Date: 08-01-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 10-2014
Publisher: College of Science for Women
Date: 23-09-2019
DOI: 10.21123/BSJ.2019.16.3(SUPPL.).0804
Abstract: Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm, to detect malicious nodes in an OBS network. The proposed semi-supervised model was trained and validated with small amount data from a selected dataset. Experiments show that the model can classify the nodes into either behaving or not-behaving classes with 90% accuracy when trained with just 20% of data. When the nodes are classified into behaving, not-behaving and potentially not-behaving classes, the model shows 65.15% and 71.84% accuracy if trained with 20% and 30% of data respectively. Comparison with some notable works revealed that the proposed model outperforms them in many respects.
Publisher: Springer Science and Business Media LLC
Date: 28-03-2022
Publisher: IEEE
Date: 28-11-2020
Publisher: MDPI AG
Date: 04-08-2021
DOI: 10.3390/COMPUTERS10080095
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.
Publisher: IEEE
Date: 02-2017
Publisher: IEEE
Date: 10-2007
Publisher: Institute of Advanced Engineering and Science
Date: 02-2019
DOI: 10.11591/IJEECS.V13.I2.PP643-648
Abstract: Genetic Algorithm (GA) is a popular desire for the researchers for creating an automated cryptanalysis system. GA strategy is useful for many problems. Genetic Algorithms try to solve problems by using genetic processes. Different techniques for deciding on fitness function relying on the ciphers have proposed by different researchers. The most necessary component is to set such a fitness function that can evaluate different types of ciphers on the identical scale. In this paper, we have proposed a combined fitness function that is valid for great sorts of ciphers. We use GA to select the fitness function. We have bought the higher result after imposing our proposed method.
Publisher: IEEE
Date: 05-01-2021
Publisher: IEEE
Date: 02-2019
Publisher: Springer International Publishing
Date: 21-10-2022
Publisher: IEEE
Date: 02-2018
Publisher: Springer Singapore
Date: 05-10-2018
Location: Bangladesh
Location: Bangladesh
No related grants have been discovered for Md Mokammel Haque.