Week 1 Review: 6/4 - 6/8

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During the first week, most of my time has been spent on learning about what my project is and all the tools I will need to use to complete it. I will be doing a project on using clustering methods and machine learning to accurately identify network anomalies, such as cyberattacks, device configuration failure, and others.

To accomplish this, I will be using the coding language "Python." I am entirely new to the coding world and have spent a lot of effort just to learn the basics in a timely manner. Some algorithms I may test are PCA, t-SNE, k-NN, k-means, and DBSCAN.

Throughout the week, I familiarized myself with the fundamentals of PCA. On Friday, I ran some code on sample data from the popular "KDD Cup 1999 Data" set. I used k-NN, PCA, and k-means to analyze the data.
 A graphical result of PCA analysis. Blue are "normal" status points and red are "attack" status points.
This is the same scatter plot as tho top without coloration.

On Thursday we took a fascinating tour of the Lawrence Berkeley National Lab NERSC center. It houses Cori and Edison, two massive super computers that are used by scientist all over the world to run very complicated simulations. These computers are basically a huge culmination of smaller computers all working together. Because of this, they can operate the computers so that different processes are running simultaneously in separate sections of the computer.


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