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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/9783
Title: Machine-Learning-Assisted Security and Privacy Provisioning for Edge Computing: A Survey
Authors: Chamola, Vinay
Keywords: EEE
Attacks
Deep Learning
Edge computing (EC)
Machine Learning
Privacy
Security
Issue Date: Jul-2021
Publisher: IEEE
Abstract: Edge computing (EC), is a technological game changer that has the ability to connect millions of sensors and provide services at the device end. The broad vision of EC integrates storage, processing, monitoring, and control of operations in the Edge of the network. Though EC provides end-to-end connectivity, speeds up operation, and reduces latency of data transfer, security is a major concern. The tremendous growth in the number of Edge Devices and the amount of sensitive information generated at the device and the cloud creates a broad surface of attack and therefore, the need to secure the static and mobile data is imperative. This article is a comprehensive survey that describes the security and privacy issues in various layers of the EC architecture that result from the networking of heterogeneous devices. Second, it discusses the wide range of machine learning and deep learning algorithms that are applied in EC use cases. Following this, this article broadly details the different types of attacks that the Edge network confronts, and the intrusion detection systems and the corresponding machine learning algorithms that overcome these security and privacy concerns. The details of machine learning and deep learning techniques for EC security are tabulated. Finally, the open issues in securing Edge networks and future research directions are provided.
URI: https://ieeexplore.ieee.org/abstract/document/9490350
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9783
Appears in Collections:Department of Electrical and Electronics Engineering

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