Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning

Abstract

While mobile social networks (MSNs) enrich people's lives, they also bring many security issues. Many attackers spread malicious URLs through MSNs, which causes serious threats to users' privacy and security. In order to provide users with a secure social environment, many researchers make great efforts. The majority of existing work is aimed at deploying a detection system on the server and classifying messages or users in MSNs through graph-based algorithms, machine learning or other methods. However, as a kind of instant messaging service, MSNs continually generate a large amount of user data. Without affecting the user experience, with existing detection mechanisms it is difficult to implement real-time detection in practical applications. In order to realize real-time message detection in MSNs, we can build more powerful server clusters or improve the utilization rate of computing resources. Assuming that computing resources of servers are limited, we use edge computing to improve the utilization rate of computing resources. In this article, we propose a multistage and elastic detection framework based on deep learning, which sets up a detection system at the mobile terminal and the server, respectively. Messages are first detected on the mobile terminal, and then the detection results are forwarded to the server along with the messages. We also design a detection queue, according to which the server can detect messages elastically when computing resources are limited, and more computing resources can be used for detecting more suspicious messages. We evaluate our detection framework on a Sina Weibo dataset. The results of the experiment show that our detection framework can improve the utilization rate of computing resources and can realize real-time detection with a high detection rate at a low false positive rate.