We will realize our goal of understanding and modeling software vulnerabilities using a machine learning technique known as a Self Organizing Feature map (SOM). SOM forms a topological ordering of the data as well as clustering it. Since my mentor has used this technique in previous works, I started the week by reading various papers written by her graduate students that have used SOMs in their research, a few of which are listed below. Below these (first three papers), I have also provided links to two papers on vulnerability analysis. Click on the title to access paper.

Luo X., Zincir-Heywood A. N., “A Comparison of SOM Based Document Categorization Systems”

Kayacik H. G., Zincir-Heywood A. N., “Using Self-Organizing Maps to Build an Attack Map for Forensic Analysis”

Yeloglu O., Zincir-Heywood A. N., Heywood M. I., "Growing Recurrent Self Organizing Map"

Ammann, P., Wijesekera, D., & Kaushik, S., "Scalable, Graph-Based Network Vulnerability Analysis"

Jajodia, S., Noel, S., & O’Berry, B., "Topological Analysis of Network Attack Vulnerability"

I also looked into the SOM toolbox that we will be using, a package created for use with MatLab. The SOM toolbox is freely available online HERE.