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Hybrid Intelligence for Smarter Networking Operations

Hybrid Intelligence for Smarter Networking Operations

Bassem Mahmoud Mokhtar, Mohamed Eltoweissy
Copyright: © 2018 |Pages: 33
ISBN13: 9781522525899|ISBN10: 1522525890|EISBN13: 9781522525905
DOI: 10.4018/978-1-5225-2589-9.ch011
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MLA

Mokhtar, Bassem Mahmoud, and Mohamed Eltoweissy. "Hybrid Intelligence for Smarter Networking Operations." Smart Technologies: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2018, pp. 238-270. https://doi.org/10.4018/978-1-5225-2589-9.ch011

APA

Mokhtar, B. M. & Eltoweissy, M. (2018). Hybrid Intelligence for Smarter Networking Operations. In I. Management Association (Ed.), Smart Technologies: Breakthroughs in Research and Practice (pp. 238-270). IGI Global. https://doi.org/10.4018/978-1-5225-2589-9.ch011

Chicago

Mokhtar, Bassem Mahmoud, and Mohamed Eltoweissy. "Hybrid Intelligence for Smarter Networking Operations." In Smart Technologies: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 238-270. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2589-9.ch011

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Abstract

The ever-growing and ever-evolved Internet targets supporting billions of networked entities to provide a wide variety of services and resources. Such complexity results in network-data from different sources with special characteristics, such as widely diverse users, multiple media, high-dimensionality and various dynamic concerns. With huge amounts of network-data with such characteristics, there are significant challenges to a) recognize emergent and anomalous behavior in network-traffic and b) make intelligent decisions for efficient network operations. Endowing the semantically-oblivious Internet with Intelligence would advance the Internet capability to learn traffic behavior and to predict future events. In this chapter, the authors discuss and evaluate the hybridization of monolithic intelligence techniques in order to achieve smarter and enhanced networking operations. Additionally, the authors provide systematic application-agnostic semantics management methodology with efficient processes for extracting and classifying high-level features and reasoning about rich semantics.

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