Design and Analysis of Anomaly Detection and Mitigation Schemes for Distributed Denial of Service Attacks in Software Defined Network. An Investigation into the Security Vulnerabilities of Software Defined Network and the Design of Efficient Detection and Mitigation Techniques for DDoS Attack using Machine Learning Techniques
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PhD Thesis (3.049Mb)
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Publication date
2019Author
Sangodoyin, Abimbola O.Supervisor
Awan, Irfan U.Hu, Yim Fun
Pillai, Prashant
Keyword
Software Defined Networks (SDN)Distributed Denial of Service (DDoS) attacks
Network security
Attack detection
Attack mitigation
Controller
Rights
The University of Bradford theses are licenced under a Creative Commons Licence.
Institution
University of BradfordDepartment
Faculty of Engineering and InformaticsAwarded
2019
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Software Defined Networks (SDN) has created great potential and hope to overcome the need for secure, reliable and well managed next generation networks to drive effective service delivery on the go and meet the demand for high data rate and seamless connectivity expected by users. Thus, it is a network technology that is set to enhance our day-to-day activities. As network usage and reliance on computer technology are increasing and popular, users with bad intentions exploit the inherent weakness of this technology to render targeted services unavailable to legitimate users. Among the security weaknesses of SDN is Distributed Denial of Service (DDoS) attacks. Even though DDoS attack strategy is known, the number of successful DDoS attacks launched has seen an increment at an alarming rate over the last decade. Existing detection mechanisms depend on signatures of known attacks which has not been successful in detecting unknown or different shades of DDoS attacks. Therefore, a novel detection mechanism that relies on deviation from confidence interval obtained from the normal distribution of throughput polled without attack from the server. Furthermore, sensitivity analysis to determine which of the network metrics (jitter, throughput and response time) is more sensitive to attack by introducing white Gaussian noise and evaluating the local sensitivity using feed-forward artificial neural network is evaluated. All metrics are sensitive in detecting DDoS attacks. However, jitter appears to be the most sensitive to attack. As a result, the developed framework provides an avenue to make the SDN technology more robust and secure to DDoS attacks.Type
ThesisQualification name
PhDCollections
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