{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:13:04Z","timestamp":1760235184770,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/computers10080095","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T21:44:24Z","timestamp":1628113464000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Comparative Analysis of Semi-Supervised Learning in Detecting Burst Header Packet Flooding Attack in Optical Burst Switching Network"],"prefix":"10.3390","volume":"10","author":[{"given":"Md. Kamrul","family":"Hossain","sequence":"first","affiliation":[{"name":"Institute of Information and Communication Technology, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3396-6568","authenticated-orcid":false,"given":"Md. Mokammel","family":"Haque","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-7509","authenticated-orcid":false,"given":"M. Ali Akber","family":"Dewan","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Edmonton, AB T5J 3S8, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MNET.2016.7389827","article-title":"Data mining algorithms for communication networks control: Concepts, survey and guidelines","volume":"30","author":"Bisio","year":"2016","journal-title":"IEEE Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"52523","DOI":"10.1109\/ACCESS.2021.3069210","article-title":"Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges","volume":"9","author":"Ridwan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Boutaba, R., Salahuddin, M.A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., and Caicedo, O.M. (2018). A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. J. Internet Serv. Appl., 1\u201399.","DOI":"10.1186\/s13174-018-0087-2"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.30534\/ijeter\/2020\/04892020","article-title":"The impacts of burst assembly parameters on optical burst switching network performance","volume":"8","year":"2020","journal-title":"Int. J. Emerg. Trends Eng. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rajab, A., Huang, C.T., Alshargabi, M., and Cobb, J. (2016, January 16\u201318). Countering burst header packet flooding attack in optical burst switching network. Proceedings of the International Conference on Information Security Practice and Experience, Zhangjiajie, China.","DOI":"10.1007\/978-3-319-49151-6_22"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"804","DOI":"10.21123\/bsj.2019.16.3(Suppl.).0804","article-title":"A Semi-Supervised Machine Learning Approach Using K-Means Algorithm to Prevent Burst Header Packet Flooding Attack in Optical Burst Switching Network","volume":"16","author":"Hossain","year":"2019","journal-title":"Baghdad Sci. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hossain, M.K., and Haque, M.M. (2020). A semi-supervised approach to detect malicious nodes in OBS network dataset using gaussian mixture model. Lecture Notes in Networks and Systems,  Springer.","DOI":"10.1007\/978-981-15-0146-3_66"},{"key":"ref_8","first-page":"4340","article-title":"Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network","volume":"10","author":"Hossain","year":"2020","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/JSACOCN.2008.033508","article-title":"Loss Classification in Optical Burst Switching Networks using Machine Learning Techniques: Improving the Performance of TCP","volume":"26","author":"Jayaraj","year":"2008","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_10","unstructured":"Levesque, M., and Elbiaze, H. (December, January 30). Graphical Probabilistic Routing Model for OBS Networks with Realistic Traffic Scenario. Proceedings of the IEEE Global Telecommunications Conference, Honolulu, HI, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1109\/TNET.2009.2031555","article-title":"A new approach to optical networks security: Attack-aware routing and wavelength assignment","volume":"18","author":"Chen","year":"2010","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"McGregor, A., Hall, M., Lorier, P., and Brunskill, J. (2004). Flow Clustering Using Machine Learning Techniques, Springer.","DOI":"10.1007\/978-3-540-24668-8_21"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/SURV.2008.080406","article-title":"A survey of techniques for internet traffic classification using machine learning","volume":"10","author":"Nguyen","year":"2008","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11107-014-0484-9","article-title":"BHP flooding vulnerability and countermeasure","volume":"29","author":"Sliti","year":"2015","journal-title":"Photonic Netw. Commun."},{"key":"ref_15","unstructured":"Sliti, M., Hamdi, M., and Boudriga, N. (July, January 27). A novel optical firewall architecture for burst switched networks. Proceedings of the 12th International Conference on Transparent Optical Networks, Munich, Germany."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.osn.2018.03.001","article-title":"Decision tree rule learning approach to counter burst header packet flooding attack in Optical Burst Switching network","volume":"29","author":"Rajab","year":"2018","journal-title":"Opt. Switch. Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.procs.2018.10.337","article-title":"Burst header packet flood detection in optical burst switching network using deep learning model","volume":"143","author":"Sattar","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s10115-018-1306-7","article-title":"Variational data generative model for intrusion detection","volume":"60","author":"Carro","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_19","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","volume":"4","author":"Kingma","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1016\/j.patrec.2008.01.030","article-title":"A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system","volume":"29","author":"Li","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, B., Spencer, B., Ling, C.X., and Zhang, H. (2008). Semi-supervised self-training for sentence subjectivity classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-540-68825-9_32"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s13042-015-0328-7","article-title":"Semi-supervised self-training for decision tree classifiers","volume":"8","author":"Tanha","year":"2017","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","article-title":"Unsupervised K-means clustering algorithm","volume":"8","author":"Sinaga","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Marwala, T. (2018). Gaussian Mixture Models. Handbook of Machine Learning, World Scientific.","DOI":"10.1142\/11013"},{"key":"ref_26","unstructured":"Xuan, G., Zhang, W., and Chai, P. (2001, January 7\u201310). EM algorithms of Gaussian mixture model and Hidden Markov Model. Proceedings of the IEEE International Conference on Image Processing, Thessaloniki, Greece."},{"key":"ref_27","first-page":"337","article-title":"Mixture models and the EM algorithm","volume":"1","author":"Murphy","year":"2012","journal-title":"Mach. Learn Probabilistic Perspect."},{"key":"ref_28","first-page":"1106","article-title":"Exploiting subjectivity classification to improve information extraction","volume":"1","author":"Riloff","year":"2005","journal-title":"Proc. Natl. Conf. Artif. Intell."},{"key":"ref_29","unstructured":"Zhu, X. (2005). Semi-Supervised Learning Literature Survey, University of Wisconsin-Madison."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_31","unstructured":"Kozodoi, N., Katsas, P., Lessmann, S., Moreira-Matias, L., and Papakonstantinou, K. (2020). Shallow Self-learning for Reject Inference in Credit Scoring. Lecture Notes in Computer Science, Springer."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Blum, A., and Mitchell, T. (1998, January 24\u201326). Combining labeled and unlabeled data with co-training. Proceedings of the Annual ACM Conference on Computational Learning Theory, Madison, WI, USA.","DOI":"10.1145\/279943.279962"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Caussinus, H., Ettinger, P., and Tomassone, R. (1982). Updating Formulae and a Pairwise Algorithm for Computing Sample Variances. COMPSTAT 1982 5th Symposium Held at Toulouse 1982, Physica.","DOI":"10.1007\/978-3-642-51461-6"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s10994-010-5221-8","article-title":"Dual coordinate descent methods for logistic regression and maximum entropy models","volume":"85","author":"Yu","year":"2011","journal-title":"Mach. Learn."},{"key":"ref_36","first-page":"1144","article-title":"Random Forest Classifier: A Survey and Future Research Directions","volume":"36","author":"Kullarni","year":"2013","journal-title":"Int. J. Adv. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.envpol.2018.01.088","article-title":"Quadratic discriminant analysis model for assessing the risk of cadmium pollution for paddy fields in a county in China","volume":"236","author":"Wang","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_40","unstructured":"(2019, November 17). Anaconda for Python. Available online: https:\/\/www.anaconda.com\/distribution\/."},{"key":"ref_41","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","unstructured":"(2019, January 09). OBS Network Dataset. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets\/Burst+Header+Packet+%28BHP%29+flooding+attack+on+Optical+Burst+Switching+%28OBS%29+Network."},{"key":"ref_43","unstructured":"(2021, August 02). NCTUns Network Simulator and Emulator. Available online: http:\/\/www.estinet.com\/ns\/?page_id=21140."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"123","DOI":"10.4186\/ej.2014.18.3.123","article-title":"E-mail spam filtering by a new hybrid feature selection method using Chi2 as filter and random tree as wrapper","volume":"18","author":"Pourhashemi","year":"2014","journal-title":"Eng. J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cao, H., Li, J., and Wang, R. (2016). An improved self-structuring neural network. Trends and Applications in Knowledge Discovery and Data Mining PAKDD 2016, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-42996-0"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.chemolab.2017.12.004","article-title":"Multivariate comparison of classification performance measures","volume":"174","author":"Ballabio","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_47","first-page":"5666","article-title":"Cost-sensitive learning with noisy labels","volume":"18","author":"Natarajan","year":"2018","journal-title":"J. Mach. Learn. Res."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/8\/95\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:40:36Z","timestamp":1760164836000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/8\/95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,4]]},"references-count":47,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["computers10080095"],"URL":"https:\/\/doi.org\/10.3390\/computers10080095","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2021,8,4]]}}}