Beeline - Cross-Network Identification of Users for Counter-Terrorism: Leveraging Open-Source Intelligence and Character Data Profiling

这个博士研究项目旨在开发新的方法来识别跨多个在线网络和平台从事潜在恐怖活动的个人。挑战在于这些个人经常使用不同的用户名和访问点，这使分析和跟踪他们的工作变得复杂。基于开源智能（OSINT）技术和性格数据分析，本研究将设计创新的计算模型，以有效地分析和跟踪不同网络中的单个个体。它将解决多源用户识别方面的研究差距，重点是整合不同的在线行为模式。该项目将包括广泛的文献综述、数据收集、算法的开发和测试，以及模型有效性的验证。预期的结果是建立一个强有力的框架，通过提高识别数字领域潜在威胁的准确性和效率来加强反恐措施。这项研究对国家安全、执法和互联网治理具有重要意义，最终有助于建立更安全的在线环境。

This PhD research project aims to develop novel methods for identifying individuals engaged in potential terrorist activities across multiple online networks and platforms. The challenge lies in the fact that these individuals often use varying usernames and access points, complicating efforts to profile and track them. Building upon open-source intelligence (OSINT) techniques and character data profiling, this study will design innovative computational models to effectively profile and track a single individual across diverse networks. It will address the research gap in multi-source user identification, focusing on integrating disparate online behavioural patterns. The project will involve an extensive literature review, data collection, development and testing of algorithms, and validation of the model's effectiveness. The expected outcome is a robust framework that enhances counter-terrorism measures by improving the accuracy and efficiency of identifying potential threats in the digital sphere. This research holds significant implications for national security, law enforcement, and internet governance, ultimately contributing to safer online environments.