SBE: Small: Behavioral Control of Deceivers in Online Attacks

SBE：小：在线攻击中欺骗者的行为控制

• 批准号: 1912898
• 负责人: Lina Zhou
• 金额: $ 11.23万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912898&HistoricalAwards=false
• 关键词: SBE; Small; Behavioral; Control; Deceivers

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.

在线攻击不仅会造成暂时的资产损失，还会对受害者造成长期的心理或情感伤害。在线通信数据的丰富性和大规模性为检测在线攻击提供了新的机会。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。欺骗是在线交流中大多数攻击的基础，而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，并实现检测攻击的预测方法。虽然在线攻击者的一整套欺骗行为被假设为未知的，但有理由预期，攻击者比其他人更难控制某些行为。通过识别在线通信中的此类行为及其关系，该项目为开发检测在线攻击的弹性和预测方法奠定了基础，并提高了在线欺骗行为及其识别的知识水平。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材。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积极吸引研究生和本科生参与他们的研究活动，特别是努力吸引女性和代表性不足的少数群体。在线攻击者不断变化的行为可能会使现有的在线攻击解决方案迅速失效。本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相互关系，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，该项目还开发了基于自然语言处理和网络分析技术的在线通信欺骗行为自动提取技术。一些预期的进展包括：（1）通过经由行为控制的新透镜调查在线攻击中的欺骗行为来扩展欺骗理论，（2）关于如何通过识别可能逃脱攻击者的控制尝试的欺骗行为来提高在线攻击检测方法的弹性的指导方针，（3）通过探索欺骗行为之间的时间关系来预测在线通信中的攻击检测的方法，（4）从网络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。

项目成果

Detection of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning Technique

欺诈性推文检测：使用网络分析和深度学习技术的实证研究

• 发表时间: 2019
• 期刊: IEEE International Conference on Intelligence and Security Informatics
• 作者: Lim, J.;Liu, Z.;Zhou, L.
• 通讯作者: Zhou, L.

Aspect Extraction from Online Consumer Reviews with WordNet-Guided Continuous-Space Language

使用 WordNet 引导的连续空间语言从在线消费者评论中提取方面

• 发表时间: 2018
• 期刊: The 28th Annual Workshop on Information Technologies and Systems
• 作者: Tao, J.;Zhou, L.;Feeney, C.
• 通讯作者: Feeney, C.

Inconsistency Investigation between Online Review Content and Ratings

• 发表时间: 2018
• 作者: Guohou Shan;Dongsong Zhang;Lina Zhou;Lingge Suo;Jaewan Lim;Chunming Shi

A Heuristic Method for Identifying Scam Ads on Craigslist

识别 Craigslist 上诈骗广告的启发式方法

• 发表时间: 2018
• 期刊: European Intelligence & Security Informatics Conference
• 作者: Alsaleh, H.;Zhou, L.
• 通讯作者: Zhou, L.

Is Self-Citation Biased? An Investigation via the Lens of Citation Polarity, Density, and Location

• DOI: 10.1007/s10796-018-9889-9
• 发表时间: 2018-12
• 期刊: Information Systems Frontiers
• 影响因子: 5.9
• 作者: Lina Zhou;Uchechukwuka Amadi;Dongsong Zhang