Self-adaptive, deep learning model for the detection and classification of Network and Host-level attacks
Date
2019-08
Authors
Ochieng, Nelson
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Intelligent 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.
Description
Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenya
Keywords
Deep learning, Network attack detection, Host attacks