Paramita Ghosh
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Identifying and Reducing Internal Employee Threat for Lockheed Martin

Tools Used:
Isolation Forest Algorithm      R      SAS E-miner       SAS E-guide       Tableau       Gephi

This project team consisted of me, Anas Laffet, Fernando Cuen, Kaitlyn Schroeder and Preetha Pai.

Anomaly detection can provide clues about an outlying minority class in the data. In this project, we analyze a simulated dataset of employees to identify insider threats.
Since we don’t have labels we need to use unsupervised learning. Reading about the state of the art methods for anomaly detection we chose the algorithm we thought was most promising: Isolation Forest.
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The "Isolation Forest" algorithm finds anomalies by deliberately “overfitting” models that memorize each data point. Since outliers have more empty space around them, they take fewer steps to memorize.
The algorithm is an adaptation of random forest where the decision trees are replaced by full decision trees (every leaf is a single data point) and we keep track of the path length between the root and each leaf (data point). The final measure for each data point would be the average path length. Abnormal data points should be classified easily thus the average path should be relatively short.
The following figure illustrates the intuition behind this algorithm:
Picture
The framework we built is described below:
Picture
The following Sway explains the whole project procedure
lockheed_martin_project_report_final.docx
File Size: 1892 kb
File Type: docx
Download File

Picture

contact me

Github
Mail to: prmta16@gmail.com

​© PARAMITAGHOSH 2016. ALL RIGHTS RESERVED.
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