Next Article in Journal
IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices
Next Article in Special Issue
A Trajectory Privacy Preserving Scheme in the CANNQ Service for IoT
Previous Article in Journal
Underwater Target Localization and Synchronization for a Distributed SIMO Sonar with an Isogradient SSP and Uncertainties in Receiver Locations
Open AccessArticle

Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

School of STEM, University of Washington Bothell, Bothell, WA 98011, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 1977; https://doi.org/10.3390/s19091977
Received: 16 March 2019 / Revised: 12 April 2019 / Accepted: 24 April 2019 / Published: 27 April 2019
(This article belongs to the Special Issue Security and Privacy in Internet of Things)
Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively. View Full-Text
Keywords: Internet of Things (IoT); Intrusion-Detection System (IDS); security; deep learning; machine learning Internet of Things (IoT); Intrusion-Detection System (IDS); security; deep learning; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Thamilarasu, G.; Chawla, S. Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things. Sensors 2019, 19, 1977.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop