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Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning
IEEE NETWORK ( IF 9.3 ) Pub Date : 2018-08-03 , DOI: 10.1109/mnet.2018.1700406
Bo Feng , Qiang Fu , Mianxiong Dong , Dong Guo , Qiang Li

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.

中文翻译:

通过深度学习在移动社交网络中进行多阶段弹性垃圾邮件检测

移动社交网络(MSN)丰富了人们的生活,但同时也带来了许多安全问题。许多攻击者通过MSN传播恶意URL,这严重威胁了用户的隐私和安全。为了向用户提供安全的社交环境,许多研究人员做出了巨大的努力。现有的大部分工作旨在通过基于图的算法,机器学习或其他方法,在服务器上部署检测系统,并对MSN中的消息或用户进行分类。但是,作为一种即时消息服务,MSN不断生成大量的用户数据。在不影响用户体验的情况下,利用现有的检测机制很难在实际应用中实现实时检测。为了在MSN中实现实时消息检测,我们可以构建功能更强大的服务器集群或提高计算资源的利用率。假设服务器的计算资源有限,我们使用边缘计算来提高计算资源的利用率。在本文中,我们提出了一种基于深度学习的多阶段弹性检测框架,该框架分别在移动终端和服务器上建立了一个检测系统。首先在移动终端上检测到消息,然后将检测结果与消息一起转发到服务器。我们还设计了一个检测队列,根据该队列,服务器可以在计算资源有限时灵活地检测消息,并且可以使用更多的计算资源来检测更多可疑消息。我们在新浪微博数据集上评估我们的检测框架。
更新日期:2018-08-06
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