Visible to the public Securing the Internet of Things and Wireless Sensor Networks via Machine Learning: A Survey

TitleSecuring the Internet of Things and Wireless Sensor Networks via Machine Learning: A Survey
Publication TypeConference Paper
Year of Publication2018
AuthorsMamdouh, M., Elrukhsi, M. A. I., Khattab, A.
Conference Name2018 International Conference on Computer and Applications (ICCA)
Date Publishedaug
ISBN Number978-1-5386-4371-6
KeywordsArtificial neural networks, composability, Human Behavior, Internet of Things, IoT security, learning (artificial intelligence), machine learning, machine learning algorithms, Metrics, Neural networks, physical devices, pubcrawl, Resiliency, security, security of data, security threats, sensor security, Support vector machines, Wireless sensor networks, WSN security
Abstract

The Internet of Things (IoT) is the network where physical devices, sensors, appliances and other different objects can communicate with each other without the need for human intervention. Wireless Sensor Networks (WSNs) are main building blocks of the IoT. Both the IoT and WSNs have many critical and non-critical applications that touch almost every aspect of our modern life. Unfortunately, these networks are prone to various types of security threats. Therefore, the security of IoT and WSNs became crucial. Furthermore, the resource limitations of the devices used in these networks complicate the problem. One of the most recent and effective approaches to address such challenges is machine learning. Machine learning inspires many solutions to secure the IoT and WSNs. In this paper, we survey the different threats that can attack both IoT and WSNs and the machine learning techniques developed to counter them.

URLhttps://ieeexplore.ieee.org/document/8460440
DOI10.1109/COMAPP.2018.8460440
Citation Keymamdouh_securing_2018