SBE: Small: Behavioral Control of Deceivers in Online Attacks

网络攻击不仅会造成暂时的资产损失，还会给受害者造成长期的心理或情感伤害。网络通信数据的丰富和大规模为检测网络攻击开辟了新的机遇。然而，攻击者的动机是不断调整他们的行为，以适应安全操作的变化，以逃避检测。在网络交流中，大多数攻击都是以欺骗为基础的，人们对欺骗的识别能力很差。在此背景下，该项目旨在提高解决方案对在线攻击的弹性，并启用预测方法来检测它们。虽然假设网络攻击者的一套完整的欺骗行为是未知的，但有理由预计一些行为比其他行为更难控制。通过识别在线交流中的此类行为及其关系，该项目为开发具有弹性和预测性的在线攻击检测方法奠定了基础，并提高了对在线欺骗行为及其识别的认识状况。在教育方面，该项目为丰富网络安全和相关学科的课程提供了新的教育材料。这项工作的跨学科性质有助于培养研究生，培养能够使用各种研究方法进行多学科前沿研究的新一代科学家。PI积极地让研究生和本科生参与他们的研究活动，特别是大力吸引女性和代表不足的少数群体。在线攻击者不断演变的行为可能会使现有的网络攻击解决方案很快变得无效。该项目不仅从网络交流的话语和结构中发现新的欺骗行为及其关系，而且通过比较不同类型的网络欺骗行为来确定攻击者在网络攻击中的行为控制。此外，该项目还开发了通过自然语言处理和网络分析技术从在线交流中自动提取欺骗行为的技术。预期的进展包括：(1)通过行为控制的新视角研究在线攻击中的欺骗行为来扩展欺骗理论；(2)指导如何通过识别可能逃脱攻击者控制企图的欺骗行为来提高在线攻击检测方法的弹性；(3)通过探索欺骗行为之间的时间关系来预测在线交流中的攻击检测方法；以及(4)从在线话语和结构中提取欺骗行为的技术。该项目可以为在线攻击检测提供综合有效的方法。

Online attacks can cause not only temporary asset loss, but long-term psychological or emotional harm to victims as well. The richness and large scale of online communication data open up new opportunities for detecting online attacks. However, attackers are motivated to constantly adapt their behaviors to changes in security operations to evade detection. Deception underlies most attacks in online communication, and people are poor at detecting deception. Against this backdrop, this project aims to improve the resilience of solutions to online attacks and enable predictive methods for their detection. Although a complete set of deception behaviors of online attackers is assumed to be unknown, there is a reason to expect that some behaviors are more difficult for attackers to control than others. By identifying such behaviors and their relations in online communication, the project lays the groundwork for the development of resilient and predictive approaches to the detection of online attacks, and advances the state of knowledge on online deception behavior and its identification. At the educational front, the project provides new educational material for enriching the curriculum in cyber security and related disciplines. The interdisciplinary nature of this work contributes to graduate student training toward a new generation of scientists who are capable of conducting multi-disciplinary cutting-edge research using a variety of research methods. The PIs actively engage students at both graduate and undergraduate levels in their research activities, particularly making a strong effort to engage women and underrepresented minorities.Online attackers' evolving behaviors can make the existing solutions to online attacks become ineffective quickly. This project not only discovers new deception behaviors and their relations from the discourse and structure of online communication, but also determines attackers' behavioral control during online attacks by comparing different types of online deception behavior. Further, this project develops techniques for automatic extraction of deception behaviors from online communication by building upon natural language processing and network analysis techniques. Some anticipated advances include: (1) deception theory extension by investigating deception behavior in online attacks via a new lens of behavior control, (2) guidelines on how to improve the resilience of online attack detection methods by identifying deception behaviors that likely escape the attackers' control attempt, (3) a predictive approach to attack detection in online communication by exploring the temporal relationships among deception behaviors, and (4) techniques for extracting deception behaviors from online discourse and structure. This project can lead to integrative and effective methods for online attack detection.