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Combining advanced computational social science and graph theoretic techniques to reveal adversarial information operations

机译：结合先进的计算社会科学和图形理论技术来揭示对抗信息操作

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Social media has influenced socio-political aspects of many societies around the world. It is an effortless way for people to enhance their communication, connect with like-minded people, and share ideas. Online social networks (OSNs) can be used for noble causes by bringing together communities with common shared interests and to promote awareness of various causes. However, there is a dark side to the use of OSNs. OSNs can also be used as a coordination and amplification platform for attacks. For instance, adversaries can increase the impact of an attack by causing panic in an area by promoting attacks using OSNs. Public data can help adversaries to determine the best timing for attacks, scheduling attacks, and then using OSNs to coordinate attacks on networks or physical locations. This convergence of the cyber and physical worlds is known as cybernetics. In this paper, we introduce an integrated method to identify malicious behavior and the actors responsible for propagating this behavior via online social networks. Throughout history we have used surveillance techniques to monitor negative behavior, activities, and information. Quantitative socio-technical methods such as deviant cyber flash mob (DCFM) detection and focal structure analysis (FSA) can provide reconnaissance capabilities that enable cities and governments to look beyond internal data and identify threats based on active events. Groups of powerful hackers can be identified through FSA which is an integrated model that uses a betweenness centrality method at the node-level and spectral modularity at group-level to identify a hidden malicious and powerful focal structure (a subset of the network). Assessment of groups using DCFM methods can help to identify powerful actors and prevent attacks. In this study, we examine multiple data sets integrating the DCFM and FSA models to help cybersecurity experts see a better picture of the threat which will help to plan a better response.
机译：社交媒体影响了世界各地许多社会的社会政治方面。对于人们加强他们的沟通，与志同道合的人联系并分享想法是一种轻松的方式。在线社交网络（OSNS）可以通过将社区带入共同共享利益的社区并促进对各种原因的认识来实现崇高的原因。但是，使用osn的暗淡方面。 OSN也可以用作攻击的协调和放大平台。例如，通过使用OSNS促进攻击，通过促进攻击来增加攻击对攻击的影响。公共数据可以帮助对手确定攻击，调度攻击的最佳时间，然后使用OSNS协调网络或物理位置的攻击。这种网络和物理世界的融合被称为Cyber​​ Netics。在本文中，我们介绍了一种综合方法来识别恶意行为和负责通过在线社交网络传播这种行为的演员。在整个历史中，我们使用监控技术来监测负面行为，活动和信息。诸如异常网络闪存（DCFM）检测和局灶性结构分析（FSA）的定量社会技术方法可以提供侦察能力，使城市和政府能够超越内部数据并根据活动事件识别威胁。可以通过FSA识别出强大的黑客组，该FSA是一个集成模型，它在组级别的节点级和频谱模块间使用之间使用之间的频谱模块，以识别隐藏的恶意和强大的焦点结构（网络的一个子集）。使用DCFM方法评估组可以有助于识别强大的演员并防止攻击。在这项研究中，我们研究了一系列数据集，整合了DCFM和FSA模型，以帮助网络安全专家看到更好地了解威胁的威胁，这将有助于规划更好的反应。

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