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dc.contributor.advisorBagula, Antoine
dc.contributor.authorMachaka, Pheeha
dc.date.accessioned2023-05-15T07:34:35Z
dc.date.available2023-05-15T07:34:35Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/11394/9947
dc.description>Magister Scientiae - MScen_US
dc.description.abstractA surge of large-scale Distributed Denial of Service (DDoS) attacks has swept the internet in recent years, possibly presenting a serious threat to industry internet service offerings. These attacks take advantage of susceptible devices linked to the internet via the Transmission Control Protocol (TCP) and the Internet Protocol (IP). As a result, current Internet-of-Things (IoT) devices are no longer off-limits. DDoS attacks have become stealthier and more sophisticated as they aim to circumvent conventional detection systems as the number of connected devices has grown. They do this by deploying both low-rate and high-rate DDoS attack techniques.en_US
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.subjectScienceen_US
dc.subjectStatistics studiesen_US
dc.subjectNetwork Information Systemsen_US
dc.subjectCybersecurityen_US
dc.subjectCyber attacken_US
dc.titleThe application of statistical and machine learning techniques for DDOS detection in network information systemsen_US
dc.rights.holderUniversity of the Western Capeen_US


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