Name
Abstract | ||
|---|---|---|
Cyber vulnerabilities pose a threat across systems in the Department of Defense. Finding ways to analyze network traffic and detect malicious behavior on a network will help keep these systems safe. This poster looks at the data collection techniques, model creation, and results of building a recurrent neural network to classify incoming traffic as normal or malicious. Additionally, it considers how the information will be best portrayed on a GUI to network administrators. The model's initial accuracy is 83.45% when trained on 500,017 connections. With increased accuracy, this tool may be used by the Department of Defense to help defend its networks.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3314058.3317291 | Proceedings of the 6th Annual Symposium on Hot Topics in the Science of Security |
Keywords | DocType | ISBN |
Bro, Zeek, recurrent neural networks | Conference | 978-1-4503-7147-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Andrews | 1 | 0 | 0.34 |
| Jennifer Behn | 2 | 0 | 0.34 |
| Danielle Jaksha | 3 | 0 | 0.34 |
| Jinwon Seo | 4 | 0 | 0.34 |
| Madeleine Schneider | 5 | 0 | 0.34 |
| James Yoon | 6 | 0 | 0.34 |
| Suzanne J. Matthews | 7 | 1 | 1.07 |
| Rajeev Agrawal | 8 | 458 | 46.34 |
| Alexander S. Mentis | 9 | 0 | 0.34 |