Deep Learning for Secure Mobile Edge Computing
This addresses security issues for mobile edge computing applications, but it is incremental as it builds on existing deep learning and machine learning approaches.
The paper tackles the problem of security threats in mobile edge computing by proposing a deep-learning-based model that uses unsupervised learning and location information to detect malicious applications, achieving an average gain of 6% accuracy compared to state-of-the-art methods.
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.