Self-adaptive, deep learning model for the detection and classification of Network and Host-level attacks

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dc.contributor.authorOchieng, Nelson
dc.date.accessioned2021-05-18T10:21:30Z
dc.date.available2021-05-18T10:21:30Z
dc.date.issued2019-08
dc.descriptionPaper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenyaen_US
dc.description.abstractIntelligent computer and network attack detection is the topic of this study. Existing classification and detection models are built using static and old datasets and hence are not self-adaptive to changing network conditions. The models are also mostly evaluated using accuracy alone. Complexity, appropriateness, execution time and understandability are not considered. It is the argument of this study that these would be quite useful and would help in determining the appropriate model that could be implemented in a vendor product. This study collects and curates its own dataset, and therefore investigates various deep learning techniques on it. The outcome is the dataset which can later be standardized as a benchmark, and a comprehensively evaluated self-adaptive model for classification and detection of network attacks.en_US
dc.description.sponsorshipFaculty of Information Technology, Strathmore University, Nairobi, Kenyaen_US
dc.identifier.urihttp://hdl.handle.net/11071/11937
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectDeep learningen_US
dc.subjectNetwork attack detectionen_US
dc.subjectHost attacksen_US
dc.titleSelf-adaptive, deep learning model for the detection and classification of Network and Host-level attacksen_US
dc.typeArticleen_US
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