Research Article
Deep Learning Techniques for Security in Edge Computing: A Detailed Survey
@INPROCEEDINGS{10.4108/eai.7-12-2021.2314539, author={R Anusuya and Karthika Renuka and S Bhuvaneshwari}, title={Deep Learning Techniques for Security in Edge Computing: A Detailed Survey}, proceedings={Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India}, publisher={EAI}, proceedings_a={ICCAP}, year={2021}, month={12}, keywords={edge computing deep learning autoencoders}, doi={10.4108/eai.7-12-2021.2314539} }- R Anusuya
Karthika Renuka
S Bhuvaneshwari
Year: 2021
Deep Learning Techniques for Security in Edge Computing: A Detailed Survey
ICCAP
EAI
DOI: 10.4108/eai.7-12-2021.2314539
Abstract
Massive amounts of data are generated instantly and as computing power gets increased subsequently the performance of cloud computing is dissatisfying. The security and privacy concerns of the user is also a serious issue. Edge computing (EC) is taken into account in recent years to resolve these issues. The major goal of this study is to know how well edge computing corresponds to the cloud and notably improves the overall performance. In the context of edge computing, the paper also shows how effective deep learning approaches are for security.
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