Title

Investigating Internet Usage Behavior Modeling: A Multifractal Approach

Presenter Information

Levi Malott

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

Levi 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 2014 and continue his education in the Computer Science Doctoral program. He is currently a Research Assistant in the Social Computing Lab under the supervision of Dr. Sriram Chellappan.

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

Comments

Joint project with Pasha Palangpour

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Apr 3rd, 1:00 PM Apr 3rd, 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.