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dc.contributor.authorWatson, Gavin
dc.date.accessioned2018-07-07T14:49:36Z
dc.date.available2018-07-07T14:49:36Z
dc.date.issued2018-07-07
dc.identifierhttps://doi.org/10.13016/M2G737680
dc.identifier.urihttp://hdl.handle.net/1903/20712
dc.description.abstractA classical multilayer perceptron algorithm and novel convolutional neural network payload classifying algorithm are presented for use on a realistic network intrusion detection dataset. The payload classifying algorithm is judged to be inferior to the multilayer perceptron but shows significance in being able to distinguish between network intrusions and benign traffic. The multilayer perceptron that is trained on less than 1% of the available classification data is judged to be a good modern estimate of usage in the real-world when compared to prior research. It boasts an average true positive rate of 94.5% and an average false positive rate of 4.68%.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesUM Computer Science Department;CS-TR-5059
dc.titleA Comparison of Header and Deep Packet Features when Detecting Network Intrusionsen_US
dc.typeTechnical Reporten_US


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