ATD: Active Learning Activity Detection in Multiplex Networks of Geospatial-Cyber-Temporal Data

与安全和威胁检测相关的数据库通常具有复杂的多模式结构，其中包含地理信息、时间戳、名称标签、人类行为模式等。组织这类数据的现代方式是以知识图的形式。如此庞大的信息结构可能包含隐藏的信息。该项目将侧重于检测组织为多路复用图的大型复杂数据结构中的活动模板。真实世界数据的例子包括交通网络、带地理标记的Twitter数据的知识图，以及模拟人类活动的合成图。该项目旨在研究一个子图匹配问题，该问题可以有一个大的组合复杂的解空间。为了费力地浏览可能与已知活动模板具有相似模式的大量信息，可能会要求主题专家提供额外的信息。这个项目将开发将人类纳入循环的算法，因此被称为主动学习方法。这项研究将解决精确和不精确的子图匹配问题。用于子图匹配的度量将包括图形拓扑测量(例如，图形编辑距离)、时间戳比较、标签属性中的相似性(例如，Levenshtein距离)和地理距离。该项目将量化模板和世界图主动学习战略。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Databases of relevance to security and threat detection often have complex multimodal structures with geographic information, timestamps, name labels, human behavior patterns, and more linked together. A modern way of organizing such data is in the form of knowledge graphs. Such large information structures can have hidden information. This project will focus on detecting templates of activity in large and complex data structures organized as a multiplex graph. Examples of real-world data include transportation networks, knowledge graphs of geotagged Twitter data, and synthetic graphs that model human activity. The project aims to investigate a subgraph matching problem that can have a large and combinatorially complex solution space. In order to wade through the myriad of information that might have similar patterns to a known activity template, subject matter experts may be called on to provide additional information. This project will develop algorithms that incorporate a human in the loop and are thus called active learning methods. The research will address both exact and inexact subgraph matching. Metrics for subgraph matching will include graph topology measurements (e.g., graph edit distance), timestamp comparisons, similarities in label attributes (e.g., Levenshtein distance), and geographic distances. The project will quantify both template and world graph active learning strategies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.