Enhancing network robustness using software-defined networking

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dc.contributor.author Li, Xin
dc.date.accessioned 2017-11-17T15:58:02Z
dc.date.available 2017-11-17T15:58:02Z
dc.date.issued 2017-12-01 en_US
dc.identifier.uri http://hdl.handle.net/2097/38236
dc.description.abstract As today's networks are no longer individual networks, networks are less robust towards failures and attacks. For example, computer networks and power networks are interdependent. Computer networks provide smart control for power networks, while power networks provide power supply. Localized network failures and attacks are amplified and exacerbated back and forth between two networks due to their interdependencies. This dissertation focuses on finding solutions to enhance network robustness. Software-defined networking provides a programmable architecture, which can dynamically adapt to any changes and can reduce the complexities of network traffic management. This architecture brings opportunities to enhance network robustness, for example, adapting to network changes, routing traffic bypassing malfunction devices, dropping malicious flows, etc. However, as SDN is rapidly proceeding from vision to reality, the SDN architecture itself might be exposed to some robustness threats. Especially, the SDN control plane is tremendously attractive to attackers, since it is the "brain" of entire networks. Thus, researching on network robustness helps protect network from a destructive disaster. In this dissertation, we first build a novel, realistic interdependent network framework to model cyber-physical networks. We allocate dependency links under a limited budget and evaluate network robustness. We further revise a network flow algorithm and find solutions to obtain a basic robust network structure. Extensive simulations on random networks and real networks show that our deployment method produces topologies that are more robust than the ones obtained by other deployment techniques. Second, we tackle middlebox chain problems using SDN. In computer networks, applications require traffic to sequence through multiple types of middleboxes to accomplish network functionality. Middlebox policies, numerous applications' requirements, and resource allocations complicate network management. Furthermore, middlebox failures can affect network robustness. We formulate a mixed-integer linear programming problem to achieve a network load-balancing objective in the context of middlebox policy chain routing. Our global routing approach manages network resources efficiently by simplifying candidate-path selections, balancing the entire network and using the simulated annealing algorithm. Moreover, in case of middlebox failures, we design a fast rerouting mechanism by exploiting the remaining link and middlebox resources locally. We implement proposed routing approaches on a Mininet testbed and evaluate experiments' scalability, assessing the effectiveness of the approaches. Third, we build an adversary model to describe in detail how to launch distributed denial of service (DDoS) attacks to overwhelm the SDN controller. Then we discuss possible defense mechanisms to protect the controller from DDoS attacks. We implement a successful DDoS attack and our defense mechanism on the Mininet testbed to demonstrate its feasibility in the real world. In summary, we vertically dive into enhancing network robustness by constructing a topological framework, making routing decisions, and protecting the SDN controller. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Software-defined networking en_US
dc.subject Network robustness en_US
dc.subject Middlebox policy en_US
dc.subject Interdependent network en_US
dc.subject Security en_US
dc.title Enhancing network robustness using software-defined networking en_US
dc.type Dissertation en_US
dc.description.degree Doctor of Philosophy en_US
dc.description.level Doctoral en_US
dc.description.department Department of Electrical and Computer Engineering en_US
dc.description.advisor Don M. Gruenbacher en_US
dc.description.advisor Caterina M. Scoglio en_US
dc.date.published 2017 en_US
dc.date.graduationmonth December en_US


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