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Deep learning-based intrusion detection for IoT networks
conference contribution
posted on 2019-01-01, 00:00 authored by Mengmeng Ge, Xiping Fu, Naeem SyedNaeem Syed, Zubair BaigZubair Baig, Gideon Teo, Antonio Robles-KellyAntonio Robles-KellyInternet of Things (IoT) has an immense potential for a plethora of applications ranging from healthcare automation to defence networks and the power grid. The security of an IoT network is essentially paramount to the security of the underlying computing and communication infrastructure. However, due to constrained resources and limited computational capabilities, IoT networks are prone to various attacks. Thus, safeguarding the IoT network from adversarial attacks is of vital importance and can be realised through planning and deployment of effective security controls; one such control being an intrusion detection system. In this paper, we present a novel intrusion detection scheme for IoT networks that classifies traffic flow through the application of deep learning concepts. We adopt a newly published IoT dataset and generate generic features from the field information in packet level. We develop a feed-forward neural networks model for binary and multi-class classification including denial of service, distributed denial of service, reconnaissance and information theft attacks against IoT devices. Results obtained through the evaluation of the proposed scheme via the processed dataset illustrate a high classification accuracy.
History
Event
IEEE Computer Society. International Symposium (24th : 2019 : Kyoto, Japan)Series
IEEE Computer Society International SymposiumPagination
256 - 265Publisher
Institute of Electrical and Electronics EngineersLocation
Kyoto, JapanPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2019-12-01End date
2019-12-03ISBN-13
978-1-7281-4961-5Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
[Unknown]Title of proceedings
PRDC 2019 : Proceedings of the 2019 IEEE 24th Pacific Rim International Symposium on Dependable ComputingUsage metrics
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