Investigating Internet Usage Behavior Modeling: A Multifractal Approach
Department
Computer Science
Major
Computer Science
Research Advisor
Chellappan, Sriram
Advisor's Department
Computer Science
Funding Source
National Science Foundation
Abstract
Multifractal analysis has been implemented in many areas of research, such as network modeling, network intrusion detection, processing neural information, DNA sequence identification, turbulence modeling and many more. The success of this approach is because of its ability to identify persistent correlations on aperiodic, highly irregular time series. Our method is to collect real Internet usage data and then apply multifractal analysis techniques for each individual user. The results will be used as a feature vector for traditional machine learning techniques to identify a specific user given unlabeled Internet data. The proposed method could be applied to the aforementioned research areas plus applications of detecting legitimacy of network users, content delivery discrimination, theft alert, target tracking and other similar areas.
Biography
Pasha is currently an undergraduate student enrolled in the Missouri University of Science and Technology Computer Science program. He is expecting to graduate in Fall of 2013 is currently a Research Assistant in the Social Computing Lab under the supervision of Dr. Sriram Chellappan. His research interests include Computer Security, Machine Leaming, and Computer Vision.
Research Category
Research Proposals
Presentation Type
Poster Presentation
Document Type
Poster
Award
Research proposal poster session, Second place
Location
Upper Atrium/Hallway
Presentation Date
03 Apr 2013, 1:00 pm - 3:00 pm
Investigating Internet Usage Behavior Modeling: A Multifractal Approach
Upper Atrium/Hallway
Multifractal analysis has been implemented in many areas of research, such as network modeling, network intrusion detection, processing neural information, DNA sequence identification, turbulence modeling and many more. The success of this approach is because of its ability to identify persistent correlations on aperiodic, highly irregular time series. Our method is to collect real Internet usage data and then apply multifractal analysis techniques for each individual user. The results will be used as a feature vector for traditional machine learning techniques to identify a specific user given unlabeled Internet data. The proposed method could be applied to the aforementioned research areas plus applications of detecting legitimacy of network users, content delivery discrimination, theft alert, target tracking and other similar areas.
Comments
Joint project with Levi Malott