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Generating synthetic data for real world detection of DoS attacks in the IoT

Lookup NU author(s): Luca Arnaboldi, Dr Charles Morisset

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Abstract

© Springer Nature Switzerland AG 2018. Denial of service attacks are especially pertinent to the internet of things as devices have less computing power, memory and security mechanisms to defend against them. The task of mitigating these attacks must therefore be redirected from the device onto a network monitor. Network intrusion detection systems can be used as an effective and efficient technique in internet of things systems to offload computation from the devices and detect denial of service attacks before they can cause harm. However the solution of implementing a network intrusion detection system for internet of things networks is not without challenges due to the variability of these systems and specifically the difficulty in collecting data. We propose a model-hybrid approach to model the scale of the internet of things system and effectively train network intrusion detection systems. Through bespoke datasets generated by the model, the IDS is able to predict a wide spectrum of real-world attacks, and as demonstrated by an experiment construct more predictive datasets at a fraction of the time of other more standard techniques.


Publication metadata

Author(s): Arnaboldi L, Morisset C

Editor(s): Mazzara, M; Ober, I; Salaün, G

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: STAF: Federation of International Conferences on Software Technologies: Applications and Foundations

Year of Conference: 2018

Pages: 130-145

Online publication date: 06/12/2018

Acceptance date: 02/04/2018

ISSN: 9783030047719

Publisher: Springer

URL: https://doi.org/10.1007/978-3-030-04771-9_11

DOI: 10.1007/978-3-030-04771-9_11

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783030047702


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