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OverviewWith the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks. This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties. Full Product DetailsAuthor: Zahir Tari (Full Professor, RMIT University, School of Computer Science, Australia) , Adil Fahad (Assistant Professor, University of Al Baha, Department of Computer Information Systems, Saudi Arabia) , Abdulmohsen Almalawi (Assistant Professor, University of King Abdulaziz, Department of Computer Science, Saudi Arabia) , Xun Yi (Professor, RMIT University, School of Computer Science, Australia)Publisher: Institution of Engineering and Technology Imprint: Institution of Engineering and Technology ISBN: 9781785619212ISBN 10: 1785619217 Pages: 288 Publication Date: 23 March 2020 Audience: College/higher education , Professional and scholarly , Tertiary & Higher Education , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsChapter 1: Introduction Chapter 2: Background Chapter 3: Related work Chapter 4: A taxonomy and empirical analysis of clustering algorithms for traffic classification Chapter 5: Toward an efficient and accurate unsupervised feature selection Chapter 6: Optimizing feature selection to improve transport layer statistics quality Chapter 7: Optimality and stability of feature set for traffic classification Chapter 8: A privacy-preserving framework for traffic data publishing Chapter 9: A semi-supervised approach for network traffic labeling Chapter 10: A hybrid clustering-classification for accurate and efficient network classification Chapter 11: ConclusionReviewsAuthor InformationZahir Tari is a full professor and discipline head of the School of Computer Science, RMIT University, Australia. His expertise is in the areas of system performance (e.g., cloud, IoT) as well as system security (e.g., SCADA, cloud). Adil Fahad is an assistant professor and head of the department of Computer Information Systems, University of Al Baha, Saudi Arabia. His research interests cover wireless sensor networks, mobile networks, SCADA security, ad-hoc networks, data mining, statistical analysis/modelling and machine learning. Abdulmohsen Almalawi is an assistant professor in the Department of Computer Science at the University of King Abdulaziz, Saudi Arabia. His research interests are in the areas of machine learning. Xun Yi is a professor at the School of Computer Science, RMIT University, Australia. His research interests include data privacy, cloud security, privacy-preserving data mining, network security protocols, applied cryptography, e-commerce security and mobile agent security. Tab Content 6Author Website:Countries AvailableAll regions |
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