Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Asher Zwickel, Junior
Lead Presenter's Name
Asher Zwickel
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Dr. Mark Grzegorzewski
Abstract
This project uses distributed computing to process and analyze large datasets related to cyber breaches and attacks. Its main goal is to find patterns between initial cyber incidents and what happens next. It looks at whether responses tend to escalate, calm down, or stay about the same over time. Understanding this helps explain how digital conflicts develop and whether they follow predictable paths. The project was built as part of university research and runs on custom software across a cluster of 17 Chromebooks. While the system can study many topics, it is currently focused on cyber activity. The software uses flexible filters to find important data without bias, so nothing useful is hidden. It tracks details like where an attack started and ended (down to the city), the path it took through different servers, and how confident the data is based on multiple sources. This information is then compared with later actions to find possible links between attacks and responses. The dataset includes global events such as cyber operations, tariffs, and location-based activity. The system only shows results when meaningful patterns or unusual cases appear based on user filters. For example, the Russia–Ukraine conflict is used as a case study, allowing users to filter by location and see how attacks may pass through other countries. It also uses known Tor bridge locations to better track hidden network routes. By finding patterns in large datasets, the project reveals trends that are hard to see manually. It can quickly detect unusual activity or hidden connections, cutting analysis time from hours to minutes. The system is available across campus, giving students and researchers a simple way to explore and understand complex cyber data.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
DCAT - distributed computing and analysis tool
This project uses distributed computing to process and analyze large datasets related to cyber breaches and attacks. Its main goal is to find patterns between initial cyber incidents and what happens next. It looks at whether responses tend to escalate, calm down, or stay about the same over time. Understanding this helps explain how digital conflicts develop and whether they follow predictable paths. The project was built as part of university research and runs on custom software across a cluster of 17 Chromebooks. While the system can study many topics, it is currently focused on cyber activity. The software uses flexible filters to find important data without bias, so nothing useful is hidden. It tracks details like where an attack started and ended (down to the city), the path it took through different servers, and how confident the data is based on multiple sources. This information is then compared with later actions to find possible links between attacks and responses. The dataset includes global events such as cyber operations, tariffs, and location-based activity. The system only shows results when meaningful patterns or unusual cases appear based on user filters. For example, the Russia–Ukraine conflict is used as a case study, allowing users to filter by location and see how attacks may pass through other countries. It also uses known Tor bridge locations to better track hidden network routes. By finding patterns in large datasets, the project reveals trends that are hard to see manually. It can quickly detect unusual activity or hidden connections, cutting analysis time from hours to minutes. The system is available across campus, giving students and researchers a simple way to explore and understand complex cyber data.