Exploring RNNs for analyzing Zeek HTTP data
Daniel Andrews, Jennifer Behn, Danielle Jaksha, Jin-Won Seo, Madeleine Schneider, James Hye Suk Yoon, Suzanne J. Matthews, Rajeev K. Agrawal, Alexander S. Mentis · 2019
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.