Deep learning-based intrusion detection for IoT networks
Document Type
Conference Proceeding
Publication Title
Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
School
ECU Security Research Institute / School of Science
RAS ID
35457
Abstract
Internet 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.
DOI
10.1109/PRDC47002.2019.00056
Access Rights
subscription content
Comments
Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., & Robles-Kelly, A. (2019, December). Deep Learning-Based Intrusion Detection for IoT Networks. In 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), Kyoto, Japan, 2019, (pp. 256-265). https://doi.org/10.1109/PRDC47002.2019.00056