  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.
