Connor Rhoades



Visualization of Deep Learning Models for Security Purposes

Insider threats pose an unavoidable risk in information security. These threats, malicious or not, leave industries and organizations susceptible to significant losses, ranging from customer information to corporate financial records. As this is a continuous threat whose victim count is ever-growing, it provides an opportunity to explore solutions for identifying these malicious events before its effects materialize. Given the success of machine learning techniques in providing powerful solutions to previously unsolvable problems, our research team is focusing on the application of neural networks on a large distributed transaction dataset made available by the Los Alamos National Laboratory. We have applied recurrent neural network algorithms on this dataset which consists of over a billion transactions to predict the next actions of different users which will lead us to detection of potential insider threats. The visualization project aims at using extracted patterns, developed by the aforementioned models, to further analyze the information by means of visualization techniques provided by the Neo4j graph database and its graphical toolset.

Such visual techniques and exploration activities will be complementary to the analytical aspects of machine learning process as they provide deep insight into the interconnections between the sequence of data in the event log. We will present different graphs with filtering capabilities to declutter the extracted patterns from machine learning stage which allows for an interactive exploration experience for the security administration of complex and large systems.



Dynamic Deep Learning Layers

Visualization of Changes in weights of edges between dense layer nodes.

This tool dynamically shows the changes in the weights on the links among nodes in different layers of a neural network by selecting a specific epoch during the training phase of the network.


Undergraduate Research at ECU

Exposing Undergrad Students to the Research Opportunities at the CS Department of ECU

This presentation included several research activities at the CS Department with emphasis on the AI and ML applications in different domains, and introducing the infrastructure available for students’ research. 

Related Research & Technology


Connor Rhoades

Connor joined the research team during his time as an undergraduate student at East Carolina University. His research interests are in the areas of visualization and cybersecurity. Connor is an applications developer from Raleigh, North Carolina. He completed his undergraduate degree at East Carolina University with a Bachelors of Science in Computer Science. Connor works to create meaningful new visualization products that can augment existing solutions to generate previously unnoticed data and insight. He is also interested in studying the role of visualizations in teaching both technically and nontechnically minded individuals

A multi-disciplinary research initiative on Information and System Intelligence research.