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dc.contributor.authorAhmad Salem, Mohammed Ali Mohammed
dc.contributor.supervisorDr Peter Dell
dc.contributor.supervisorDr Helen Armstrong

This research deals with Denial of Service (DoS) flooding attacks. These types of attacks toward internet connected networks are on the rise. The research proposes a model that triangulate between statistical and neural network forecasting approaches. The proposed model can identify DoS attacks using the firewall rejected traffic falling outside the normal levels that could indicate a DoS attack. The triangulation approach provided the research model with multi prediction techniques with high accuracy.

dc.publisherCurtin University
dc.titleDoSTDM: A denial of service detection model using firewall data traffic pattern matching
curtin.departmentSchool of Information Systems
curtin.accessStatusOpen access

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