الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The internet and cloud infrastructure are undergoing a massive change in the last decade. Which lead to the need of Secure software for automated high-performance networks & clouds. Network traffic classification plays an increasingly key role in this rapidly changing environment. where in the old days, telecommunication networks were characterized by nonflexible protocols and hardware devices strongly regulated by the big vendors in the field, current telecom network is controlled by Software Defined Networking or SDN and network traffic management or deep network traffic inspection is implemented as Deep packet inspection servers in data centers. As a result, network services continuously offer new features, have non-stop increase in performance, and are continuously perfecting their resources. Which introduce not only new possibilities but also significant research challenges for future telecommunication networks and applications researchers. Software-based networks are a two-sided sword, with flexibility and agility on one side, and uncertain reliability/performance on the other side. Although new network services or control mechanisms can be rapidly developed in software and deployed on virtual machines or containers, it is an open challenge to ensure that these efforts will result into available networks/services, producing maximal performance with minimal resources in dynamically changing environments. Combining ultramodern software development with deep insight in internet technologies, cloud-based services, and their associated control mechanisms.
The thesis is divided into 6 chapters including lists of contents, tables, and figures as well as list of references.
Chapter 1
This chapter is an Introduction including the motivation for this work, followed by the thesis outline and contributions.
Chapter 2
This chapter includes a literature review on methods for traffic classification with focus on machine learning techniques and classified traffic types.
Chapter 3
This chapter proposes a new solution of ensemble-based network traffic classification. Simulation results are shown at the end of this chapter
Chapter 4
This chapter presents deployments and applications of market NDR comparing different traffic classification models.
Chapter 5
This chapter Concludes the work of this thesis work. Suggested future work is presented.