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Network Classification for Traffic Management Anomaly detection, feature selection, clustering and classification

Tari Zahir Yi Xun
Innbundet / 2020 / Engelsk

Produktdetaljer

ISBN
9781785619212
Publisert
2020-03-23
Utgiver
Vendor
Institution of Engineering and Technology
Høyde
234 mm
Bredde
156 mm
Aldersnivå
U, P, 05, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
288

Forfatter
Tari Zahir
Yi Xun

Biographical note

Zahir 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.

Network Classification for Traffic Management Anomaly detection, feature selection, clustering and classification

Tari Zahir Yi Xun
Innbundet / 2020 / Engelsk
Tari Zahir Yi Xun
Innbundet / 2020 / Engelsk
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With 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.
Les mer
This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks.
Chapter 1: IntroductionChapter 2: BackgroundChapter 3: Related workChapter 4: A taxonomy and empirical analysis of clustering algorithms for traffic classificationChapter 5: Toward an efficient and accurate unsupervised feature selectionChapter 6: Optimizing feature selection to improve transport layer statistics qualityChapter 7: Optimality and stability of feature set for traffic classificationChapter 8: A privacy-preserving framework for traffic data publishingChapter 9: A semi-supervised approach for network traffic labelingChapter 10: A hybrid clustering-classification for accurate and efficient network classificationChapter 11: Conclusion
Les mer

Relaterte produkter

With 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.
Les mer
This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks.
Chapter 1: IntroductionChapter 2: BackgroundChapter 3: Related workChapter 4: A taxonomy and empirical analysis of clustering algorithms for traffic classificationChapter 5: Toward an efficient and accurate unsupervised feature selectionChapter 6: Optimizing feature selection to improve transport layer statistics qualityChapter 7: Optimality and stability of feature set for traffic classificationChapter 8: A privacy-preserving framework for traffic data publishingChapter 9: A semi-supervised approach for network traffic labelingChapter 10: A hybrid clustering-classification for accurate and efficient network classificationChapter 11: Conclusion
Les mer

Produktdetaljer

ISBN
9781785619212
Publisert
2020-03-23
Utgiver
Vendor
Institution of Engineering and Technology
Høyde
234 mm
Bredde
156 mm
Aldersnivå
U, P, 05, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
288

Forfatter
Tari Zahir
Yi Xun

Biographical note

Zahir 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.

Relaterte produkter