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A New Metric for the Analysis of Swarms Using Potential Fields

Eliot, Neil, Kendall, David and Brockway, Michael (2018) A New Metric for the Analysis of Swarms Using Potential Fields. IEEE Access, 6. pp. 211-63267. ISSN 2169-3536

Item Type: Article

Abstract

In the practical study of cybersecurity, students bene�t greatly from having a full control of
physical equipment and services. However, this presents far too great a risk to security to be permitted on
university campus networks. This paper describes an approach, used successfully at Northumbria University,
in which students have control of an off-campus network laboratory, with a dedicated connection to the
Internet. The laboratory is �exible enough to allow the teaching of general purpose networking and operating
systems courses, while also supporting the teaching of cybersecurity through the safe integration of honeypot
devices. In addition, this paper gives an analysis of honeypot architectures and presents two in detail. One of
these offers students the opportunity to study cybersecurity attacks and defences at very low cost. It has been
developed as a stand-alone device that can also be integrated safely into the laboratory environment for the
study of more complex scenarios. The main contributions of this paper are the design and implementation
of an off-campus, physical network laboratory; a small, low-cost, con�gurable platform for use as a ``lightweight''
honeypot; and a laboratory-based, multi-user honeypot for large-scale, concurrent, cybersecurity
experiments. This paper outlines how the laboratory environment has been successfully deployed within a
university setting to support the teaching and learning of cybersecurity. It highlights the type of experiments
and projects that have been supported and can be supported in the future.

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More Information

Uncontrolled Keywords: Measurement;Mathematical model;Analytical models;Stability analysis;Robot sensing systems;Heuristic algorithms;Swarming;swarm dynamics;swarm metric
Depositing User: Neil Eliot

Identifiers

Item ID: 17506
Identification Number: https://doi.org/10.1109/ACCESS.2018.2877421
ISSN: 2169-3536
URI: http://sure.sunderland.ac.uk/id/eprint/17506
Official URL: https://ieeexplore.ieee.org/document/8502021

Users with ORCIDS

ORCID for Neil Eliot: ORCID iD orcid.org/0000-0002-7591-7783

Catalogue record

Date Deposited: 15 Apr 2024 08:27
Last Modified: 15 Apr 2024 08:30

Contributors

Author: Neil Eliot ORCID iD
Author: David Kendall
Author: Michael Brockway

University Divisions

Faculty of Technology > School of Computer Science

Subjects

Computing > Artificial Intelligence

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