Network Traffic Anomalies, Natural Language Processing, and Random Matrix Theory

Abstract

Random Matrix Theory (RMT) is an important tool for detecting correlations in multidimensional time series, such as stock market price histories, and origin-destination flows in data networks. We review the basic theory and propose two novel applications: the detection of traffic anomalies in data networks and natural language processing. For traffic anomalies the advantage of this approach is that training sets are not necessary. In the case of natural language processing, our approach is a refinement of the standard Latent Semantic Analysis (LSA). We will demonstrate applications to real traffic from a data network, and present the use in Natural Language Processing. Directions for future work will be discussed.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
ADA619172

Entities

People

  • Ira S. Moskowitz
  • Pedro N. Safier

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Adaptive Systems
  • Applied Computer Science
  • Artificial Intelligence
  • Complex Adaptive Systems
  • Computer Languages
  • Computer Science
  • Information Operations
  • Language
  • Matrix Theory
  • Military Research
  • Natural Language Processing
  • Natural Languages
  • Standards

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Operations Research
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Machine Translation