Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning
Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning
复制标题
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
DOI：
10.1109/mnet.2018.1700406
复制
发表时间：
2018-07
期刊：
影响因子：
9.3
通讯作者：
Li Qiang
中科院分区：
文献类型：
--
作者：
Feng Bo;Fu Qiang;Dong Mianxiong;Guo Dong;Li Qiang
移动社交网络（MSN）丰富人们生活的同时，也带来了许多安全问题。许多攻击者通过MSN传播恶意URL，对用户的隐私和安全造成严重威胁。为了给用户提供一个安全的社交环境，许多研究者付出了巨大的努力。现有的大部分工作旨在在服务器上部署检测系统，并通过基于图的算法、机器学习或其他方法对 MSN 中的消息或用户进行分类。然而，MSN作为一种即时通讯服务，不断产生大量的用户数据。在不影响用户体验的情况下，现有的检测机制很难在实际应用中实现实时检测。为了实现MSN中的实时消息检测，我们可以构建更强大的服务器集群或者提高计算资源的利用率。假设服务器的计算资源有限，我们利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级弹性检测框架，分别在移动端和服务器端建立了检测系统。首先在移动终端上检测消息，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的情况下弹性地检测消息，并且可以使用更多的计算资源来检测更多的可疑消息。我们在新浪微博数据集上评估我们的检测框架。实验结果表明，我们的检测框架可以提高计算资源的利用率，并且能够以低误报率实现高检测率的实时检测。
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.
登录
查看更多内容
DOI：
--
发表时间：
2012-04
期刊：
--
影响因子：
--
作者：
Q. Cao;Michael Sirivianos;Xiaowei Yang;Tiago Pregueiro
通讯作者：
Q. Cao;Michael Sirivianos;Xiaowei Yang;Tiago Pregueiro
影响因子：
11.2
作者：
Li, He;Ota, Kaoru;Guo, Minyi
通讯作者：
Guo, Minyi
DOI：
10.1587/transinf.2013thp0011
发表时间：
2014-10
期刊：
IEICE Trans. Inf. Syst.
影响因子：
--
作者：
M. Dong;Takashi Kimata;K. Sugiura;K. Zettsu
通讯作者：
M. Dong;Takashi Kimata;K. Sugiura;K. Zettsu
DOI：
10.1007/978-3-319-33630-5_13
发表时间：
2016-05
期刊：
--
影响因子：
--
作者：
Dieudonne Mulamba;I. Ray;I. Ray
通讯作者：
Dieudonne Mulamba;I. Ray;I. Ray
DOI：
10.1109/tifs.2014.2316975
发表时间：
2014-06-01
影响因子：
6.8
作者：
Gong, Neil Zhenqiang;Frank, Mario;Mittal, Prateek
通讯作者：
Mittal, Prateek