Deep Learning for Secure Mobile Edge Computing

TL;DR

This paper presents a deep learning model that uses unsupervised learning and location data to detect security threats in mobile edge computing, significantly improving detection accuracy over existing methods.

Contribution

It introduces a novel deep learning approach leveraging location information for security threat detection in MEC environments.

Findings

Achieves 6% higher accuracy than existing algorithms

Effective in detecting malicious applications at network edge

Validated with 10 diverse datasets

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.