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Deep Learning for Secure Mobile Edge Computing - 2017

Deep Learning for Secure Mobile Edge Computing

Research Paper on Deep Learning for Secure Mobile Edge Computing

Research Area:  Edge Computing

Abstract:

Mobile edge computing (MEC) is a promising approach for enabling cloud-computing capabilities at the edge of cellular networks. Nonetheless, security is becoming an increasingly important issue in MEC-based applications. In this paper, we propose a deep-learning-based model to detect security threats. The model uses unsupervised learning to automate the detection process, and uses location information as an important feature to improve the performance of detection. Our proposed model can be used to detect malicious applications at the edge of a cellular network, which is a serious security threat. Extensive experiments are carried out with 10 different datasets, the results of which illustrate that our deep-learning-based model achieves an average gain of 6% accuracy compared with state-of-the-art machine learning algorithms.

Keywords:  
Deep Learning
Secure
Mobile Edge Computing
Edge Computing

Author(s) Name:  Yuanfang Chen, Yan Zhang, Sabita Maharjan

Journal name:  Cryptography and Security

Conferrence name:  

Publisher name:  arXiv:1709.08025

DOI:  10.48550/arXiv.1709.08025

Volume Information: