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 Название: Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification
Автор: Zahir Tari, Adil Fahad
Издательство: The Institution of Engineering and Technology
Год: 2020
Страниц: 276
Язык: английский
Формат: pdf (true), epub
Размер: 10.7 MB
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.
In recent years, knowing what information is passing through the networks is rapidly becoming more and more complex due to the ever-growing list of applications shaping today’s Internet traffic. Consequently, traffic monitoring and analysis have become crucial for tasks ranging from intrusion detection, traffic engineering to capacity planning. Network traffic classification is the process of analyzing the nature of the traffic flows on the networks, and it classifies these flows mainly on the basis of protocols (e.g., TCP, UDP, and IMAP) or by different classes of applications (e.g., HTTP, peer-to-peer (P2P), and Games). Network traffic classification has the capability to address fundamentals to numerous network-management activities for Internet Service Providers (ISPs) and their equipment vendors for better quality of service (QoS) treatment. In particular, network operators need an accurate and efficient classification of traffic for effective network planning and design, applications prioritization, traffic shaping/policing, and security control. It is essential that network operators understand the trends in their networks so that they can react quickly to support their business goals. Traffic classification can also be a part of intrusion detection systems (IDS), where the main goal of such systems is to detect a wide range of unusual or anomalous events and to block unwanted traffic.
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Machine Learning in Complex Networks Название: Machine Learning in Complex Networks Автор: Liang Zhao, Thiago Christiano Silva Издательство: Springer Год: 2016 Страниц: 331 Формат: PDF... |
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Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification 
