I am a PhD student at Georgia Institute of Technology performing research in the areas of network and computer security under the advisement of Patrick Traynor. I currently have degrees from Wake Forest University (M.S., Computer Science, ’11) and Duke University (B.S., Computer Science, ’06).
From 2006-2009, I lived and worked in Atlanta, GA as a software engineer doing development in the financial services and distributed assets industries. My first employer during that time was a smaller company, Obvient Strategies, focused on developing business intelligence and data warehousing solutions. After a year, I left Obvient for a position at Global Payments, Inc (once listed by Forbes as one of The 400 Best Big Companies) where I spent the next two years working on a web-based transactions processing platform.
Performed research into characterizing the malicious threats seen in cellular data networks.
Performed research into exploiting graph structure for identity resolution and de-anonymization.
Performed research in the area of cyber security and helped to setup and deploy a network security testbed.
Development of virtual terminal web application and payment gateway for the company's transaction processing engine.
Developed business intelligence and data warehousing solutions for the Distributed Asset Industry.
An important component of network resource management and security enforcement is recognizing the applications active on a network. Unfortunately payload encryption and the use of non-standard ports render traditional application identification methods marginally useful. Newer in-the-dark application discovery methods can contend with these conditions, but still rely on packet level information that may not be readily available to administrators.
This paper describes the initial findings and future directions of a technique that uses network motifs (e.g. overrepresented interaction subgraphs) to identify network activity. Modeling the flow-level network interactions as a graph, the proposed approach identifies sets of frequently occurring subgraphs useful to infer the applications. Initial results show this approach can achieve an average accuracy of 85% in mapping motifs to applications. We argue that performance can be improved by incorporating features into motifs that provide information about vertices and edges while preserving the ability for system administrators to gather such feature information from flow-level traces. Specific issues that arise in the collection of computer network interaction data and in dealing with the scale of such data are also highlighted.
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