Faculty Advisor

Harrison, Lane T.

Faculty Advisor

Paffenroth, Randy Clinton

Abstract

In this MQP, we focus on the development of a visualization-enabled anomaly detection system. We examine the 2011 VAST dataset challenge to efficiently generate meaningful features and apply Robust Principal Component Analysis (RPCA) to detect any data points estimated to be anomalous. This is done through an infrastructure that promotes the closing of the loop from feature generation to anomaly detection through RPCA. We enable our user to choose subsets of data through a web application and learn through visualization systems where problems are within their chosen local data slice. In this report, we explore both feature engineering techniques along with optimizing RPCA which ultimately lead to a generalized approach for detecting anomalies within a defined network architecture.

Publisher

Worcester Polytechnic Institute

Date Accepted

March 2018

Major

Computer Science

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Computer Science

Advisor Department

Mathematical Sciences

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