arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1709.08025cs.CRcs.LGcs.NI

Deep Learning for Secure Mobile Edge Computing

Hangzhou Dianzi University(杭州电子科技大学);University of Oslo(奥斯陆大学)

Yuanfang Chen, Yan Zhang, Sabita Maharjan

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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