DoSTDM: A denial of service detection model using firewall data traffic pattern matching
|dc.contributor.author||Ahmad Salem, Mohammed Ali Mohammed|
|dc.contributor.supervisor||Dr Peter Dell|
|dc.contributor.supervisor||Dr 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.title||DoSTDM: A denial of service detection model using firewall data traffic pattern matching|
|curtin.department||School of Information Systems|