Machine Learning

Deep Learning for Secure Mobile Edge Computing

Tagged: , ,

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 2 years ago.


  • arXiv
    5 pts

    Deep Learning for Secure Mobile Edge Computing

    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.

    Deep Learning for Secure Mobile Edge Computing
    by Yuanfang Chen, Yan Zhang, Sabita Maharjan
    https://arxiv.org/pdf/1709.08025v1.pdf

You must be logged in to reply to this topic.