Anomaly detection from personal usage patterns in web applications

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Date

2006

Author

Vural, Gürkan

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The anomaly detection task is to recognize the presence of an unusual (and potentially hazardous) state within the behaviors or activities of a computer user, system, or network with respect to some model of normal behavior which may be either hard-coded or learned from observation. An anomaly detection agent faces many learning problems including learning from streams of temporal data, learning from instances of a single class, and adaptation to a dynamically changing concept. The domain is complicated by considerations of the trusted insider problem (recognizing the difference between innocuous and malicious behavior changes on the part of a trusted user). This study introduces the anomaly detection in web applications and formulates it as a machine learning task on temporal sequence data. In this study the goal is to develop a model or profile of normal working state of web application user and to detect anomalous conditions as deviations from the expected behavior patterns. We focus, here, on learning models of normality at the user behavioral level, as observed through a web application. In this study we introduce some sensors intended to function as a focus of attention unit at the lowest level of a classification hierarchy using Finite State Markov Chains and Hidden Markov Models and discuss the success of these sensors.

Subject Keywords

Computer Science., Electronic Computers.

URI

http://etd.lib.metu.edu.tr/upload/12607973/index.pdf
https://hdl.handle.net/11511/16063

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

G. Vural, “Anomaly detection from personal usage patterns in web applications,” M.S. - Master of Science, Middle East Technical University, 2006.