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Lookup NU author(s): Luca Arnaboldi,
Dr Charles Morisset
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© 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.
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
Online publication date: 06/12/2018
Acceptance date: 02/04/2018
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science