Attacking strategies and temporal analysis involving Facebook discussion groups

Chun-Ming Lai, Xiaoyun Wang, Yunfeng Hong, Yu-Cheng Lin, S. F. Wu, P. Mcdaniel, H. Çam
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引用次数: 4

Abstract

Online social network (OSN) discussion groups are exerting significant effects on political dialogue. In the absence of access control mechanisms, any user can contribute to any OSN thread. Individuals can exploit this characteristic to execute targeted attacks, which increases the potential for subsequent malicious behaviors such as phishing and malware distribution. These kinds of actions will also disrupt bridges among the media, politicians, and their constituencies. For the concern of Security Management, blending malicious cyberattacks with online social interactions has introduced a brand new challenge. In this paper we describe our proposal for a novel approach to studying and understanding the strategies that attackers use to spread malicious URLs across Facebook discussion groups. We define and analyze problems tied to predicting the potential for attacks focused on threads created by news media organizations. We use a mix of macro static features and the micro dynamic evolution of posts and threads to identify likely targets with greater than 90% accuracy. One of our secondary goals is to make such predictions within a short (10 minute) time frame. It is our hope that the data and analyses presented in this paper will support a better understanding of attacker strategies and footprints, thereby developing new system management methodologies in handing cyber attacks on social networks.
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涉及Facebook讨论组的攻击策略和时间分析
网络社交网络(OSN)讨论组对政治对话的影响越来越大。在没有访问控制机制的情况下,任何用户都可以对任何OSN线程进行贡献。个人可以利用这一特性来执行有针对性的攻击,这增加了后续恶意行为的可能性,例如网络钓鱼和恶意软件分发。这些行为也会破坏媒体、政客和他们的选民之间的桥梁。对于安全管理人员来说,将恶意网络攻击与在线社交活动相结合是一个全新的挑战。在本文中,我们提出了一种新的方法来研究和理解攻击者在Facebook讨论组中传播恶意url的策略。我们定义并分析了与预测潜在攻击有关的问题,这些攻击集中在新闻媒体组织创建的线程上。我们混合使用宏观静态特征和帖子和线程的微动态演变来识别可能的目标,准确率超过90%。我们的第二个目标之一是在短时间内(10分钟)做出这样的预测。我们希望本文中提供的数据和分析将有助于更好地理解攻击者的策略和足迹,从而开发新的系统管理方法来处理社交网络上的网络攻击。
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