PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Algebraic and spectral methods for network anomaly detection. Given at: NATO school on Mining Massive Datasets for Security Italy, Sep. 2007
Naftali Tishby
In: NATO school on Mining Massive Datasets for Security, 5-29 Sep, 2007, Gazzada, Italy.

Abstract

The tutorial will discuss two central issues: (i) Information Theoretic principles and algorithms for extracting predictive statistics in distributed networks and (ii) algebraic and spectral methods for network anomaly detection. The first part will deal with the concept of predictive information - the mutual information between the past and future of a process, its sub-extensive properties, and algorithms for estimating it from data.We will argue that the information theoretic predictability quantifies the complexity of a process and provides effective ways for detecting anomalies and surprises in the process. Using the Information Bottleneck algorithms one can extract approximate sufficient statistics from the past to the future of the process and use them as anomaly detectors on multiple time scales. In the second part we will discuss ways for analyzing network activity using spectral methods (distributed PCA and network Laplacian analysis) for identifying regular temporal patterns of connected network components. By combining the two approaches, we will suggest new techniques for network anomaly detectors for security.

Other (Video)
EPrint Type:Conference or Workshop Item (Invited Talk)
Additional Information:http://videolectures.net/mmdss07_tishby_itam/
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:4083
Deposited By:Naftali Tishby
Deposited On:25 February 2008