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Robert Atkinson

Researcher at University of Strathclyde

Publications -  184
Citations -  2600

Robert Atkinson is an academic researcher from University of Strathclyde. The author has contributed to research in topics: Partial discharge & Intrusion detection system. The author has an hindex of 20, co-authored 174 publications receiving 1903 citations. Previous affiliations of Robert Atkinson include École Navale & Colorado State University.

Papers
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Proceedings ArticleDOI

Threat analysis of IoT networks using artificial neural network intrusion detection system

TL;DR: In this article, a multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks.
Posted Content

Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey

TL;DR: A taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works, and a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion Detection systems.
Journal ArticleDOI

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

TL;DR: In this paper, the authors provide researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks.
Posted Content

A taxonomy and survey of intrusion detection system design techniques, network threats and datasets

TL;DR: This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems (IDS) capabilities and assets to improve both the efficiency of IDS and the creation of datasets to build the next generation IDS as well as to reflect networks threats more accurately in future datasets.