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

摘要

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.