Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/568412
Title: Secured and load balanced content aware routing in software defined networks
Researcher: Aswini C
Guide(s): Valarmathi M L
Keywords: Aligned Investigation Technique
Information Centric Network
Software Defined Network
University: Anna University
Completed Date: 2024
Abstract: Routing in any communication network is a complicated task, due newlineto its ever-changing nature. Software Defined Network (SDN) is the widely newlineused technology in the future communication networks, that provides smart newlinerouting through OpenFlow enabled switches. Information Centric Network newline(ICN) has some unique routing features that minimize the congestion in the newlinenetwork through content awareness. This characteristic of ICN is used for newlineoptimal routing in SDN. newlineThe decoupled nature of SDN segregates data forwarding from newlinecentralized management and interactive resource sharing. With the rapid newlinegrowth in services and applications, they are vulnerable to possible network newlineattacks and face several security challenges. Intrusion detection has widely newlinebeen used to ensure network security, but classical detection methods fail to newlinedetect the attacks intelligently. newlineThe key characteristics of SDN and ICN enable the researchers to newlineconcentrate on the ways to improve the Quality of Service (QoS) newlinecharacteristics of the network. Though ICN is more advantageous, there are newlinecertain issues due to the dynamic nature of network. The concepts of Machine newlineLearning have been used in the proposed work to solve these difficulties. newlineContent Aware Routing in SDN still holds some key areas to be addressed for newlineimproving the QoS metrics of the network through content caching capability. newlineThe proposed work defends against network security attacks in a newlinemultipath routing environment and improves the utilization of bandwidth in newlinean effective manner. It also enhances the performance of load balancing in newlineSDN through the concepts of Optimization Techniques, Deep Learning, newlineReinforcement Learning and Ensemble Learning. newline newline
Pagination: xviii,149p.
URI: http://hdl.handle.net/10603/568412
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File25.58 kBAdobe PDFView/Open
02_prelimpages.pdf1.86 MBAdobe PDFView/Open
03_contents.pdf13.7 kBAdobe PDFView/Open
04_abstracts.pdf181.37 kBAdobe PDFView/Open
05_chapter1.pdf1.04 MBAdobe PDFView/Open
06_chapter2.pdf1.1 MBAdobe PDFView/Open
07_chapter3.pdf1.05 MBAdobe PDFView/Open
08_chapter4.pdf1.03 MBAdobe PDFView/Open
09_annexures.pdf111.85 kBAdobe PDFView/Open
80_recommendation.pdf181.3 kBAdobe PDFView/Open
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