首页> 外文期刊>Advances in Science, Technology and Engineering Systems >A security approach based on honeypots: Protecting Online Social network from malicious profiles
【24h】

A security approach based on honeypots: Protecting Online Social network from malicious profiles

机译:基于蜜罐的安全方法:保护在线社交网络免受恶意配置文件的攻击

获取原文
获取外文期刊封面目录资料

摘要

In the recent years, the fast development and the exponential utilization of social networks have prompted an expansion of social Computing. In social networks users are interconnected by edges or links, where Facebook, twitter, LinkedIn are most popular social networks websites. Due to the growing popularity of these sites they serve as a target for cyber criminality and attacks. It is mostly based on how users are using these sites like Twitter and others. Attackers can easily access and gather personal and sensitive user’s information. Users are less aware and least concerned about the security setting. And they easily become victim of identity breach. To detect malicious users or fake profiles different techniques have been proposed like our approach which is based on the use of social honeypots to discover malicious profiles in it. Inspired by security researchers who used honeypots to observe and analyze malicious activity in the networks, this method uses social honeypots to trap malicious users. The two key elements of the approach are: (1) The deployment of social honeypots for harvesting information of malicious profiles. (2) Analysis of the characteristics of these malicious profiles and those of deployed honeypots for creating classifiers that allow to filter the existing profiles and monitor the new profiles.
机译:近年来,社交网络的快速发展和指数利用促使社交计算的发展。在社交网络中,用户通过边缘或链接相互连接,其中Facebook,Twitter,LinkedIn是最受欢迎的社交网络网站。由于这些站点的日益普及,它们成为网络犯罪和攻击的目标。它主要取决于用户如何使用这些网站(如Twitter等)。攻击者可以轻松访问和收集个人和敏感用户的信息。用户不太了解安全性,最不用担心安全性设置。他们很容易成为身份泄露的受害者。为了检测恶意用户或伪造的个人资料,已经提出了不同的技术,例如我们的方法,该技术基于使用社交蜜罐来发现其中的恶意个人资料。受使用蜜罐观察和分析网络中恶意活动的安全研究人员的启发,此方法使用社交蜜罐来捕获恶意用户。该方法的两个关键要素是:(1)部署社交蜜罐以收集恶意配置文件的信息。 (2)分析这些恶意概要文件和已部署的蜜罐的特征,以创建分类器,从而可以过滤现有概要文件并监视新概要文件。

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