Exploring RNNs for analyzing Zeek HTTP data


Exploring RNNs for analyzing Zeek HTTP data


6th Annual Symposium on Hot Topics in the Science of Security

Contributing USMA Research Unit(s)

Cyber Research Center, Electrical Engineering and Computer Science


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.

Publication Date

Spring 4-2019

Document Type


Publisher City

Nashville, Tennessee


External Link

Download Full Text

Exploring RNNs for analyzing Zeek HTTP data

Record links to items hosted by external providers may require fee for full-text.