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DYNAMIC ANALYSIS OF PROGRAM EXECUTION TO DISCOVER USAGE CLASSES

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abstract
Dynamically predicting the behavior of applications has the potential to be useful in a variety of application management scenarios, such a job scheduling and detecting potential failure conditions. This research explores the use of machine learning techniques to predict an application’s usage class based on the analysis of its assembly-level instruction trace, working under the premise that processes which are similar in usage class will share similar low-level behavior and functionality. A small catalog of usage classes was developed. A machine learning algorithm was employed to model usage classes and then predict class label for each previously unseen application instruction traces. Various levels of performance were observed depending on the type of trace information and the machine learning algorithm employed.
subject
Assembly Instructions
Classification
Clustering
Dynamic Analysis
Machine Learning
Usage Class
contributor
Gupta, Charchil (author)
Turkett Jr., William H. (committee chair)
Fulp, Errin (committee member)
Cañas, Daniel A. (committee member)
date
2017-06-15T08:36:13Z (accessioned)
2017-06-15T08:36:13Z (available)
2017 (issued)
degree
Computer Science (discipline)
identifier
http://hdl.handle.net/10339/82245 (uri)
language
en (iso)
publisher
Wake Forest University
title
DYNAMIC ANALYSIS OF PROGRAM EXECUTION TO DISCOVER USAGE CLASSES
type
Thesis

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