• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Arabian Journal for Science and Engineering >A Feature Selection Approach to Detect Spam in the Facebook Social Network
【24h】

A Feature Selection Approach to Detect Spam in the Facebook Social Network

机译：Facebook社交网络中检测垃圾邮件的特征选择方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The widespread adoption of social networks and their enormous facilities and growing opportunities has attracted many users and audience. But along with attractive and interesting messages and topics, inappropriate and sometimes criminal contents, such as spam, are also released on these networks. Malicious spammers intend to send inaccurate or irrelevant contents to distribute malformed information on online social networks. This paper is about the spam comments detection on the Facebook social network. By reviewing the posts and comments, and studying their features, an online spam filtering system has been designed in this paper. The proposed filtering system is able to exploit various exploration methods and optimization algorithms such as simulated annealing, particle swarm optimization, ant colony optimization, and differential evolution to detect and filter malicious contents and to prevent publishing spam comments to provide a secure environment for users of this popular social network. Furthermore, supervised machine learning methods, clustering techniques, and decision trees have been exploited to provide an accurate performance and appropriate speed for the proposed filtering system.
机译：社交网络的广泛采用及其巨大的功能和不断增长的机会吸引了许多用户和受众。但是，除了诱人和有趣的消息和主题之外，这些网络上还发布了不适当的，有时是犯罪的内容（例如垃圾邮件）。恶意垃圾邮件发送者打算发送不正确或不相关的内容，以在在线社交网络上分发格式错误的信息。本文是关于Facebook社交网络上垃圾邮件评论检测的。通过查看帖子和评论并研究其功能，设计了一种在线垃圾邮件过滤系统。所提出的过滤系统能够利用各种探索方法和优化算法（例如模拟退火，粒子群优化，蚁群优化和差异进化）来检测和过滤恶意内容并防止发布垃圾邮件评论，从而为用户提供安全的环境这个受欢迎的社交网络。此外，已利用监督的机器学习方法，聚类技术和决策树为建议的过滤系统提供准确的性能和适当的速度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号