Publication:
MACHINE LEARNING STATISTICAL DETECTION OF ANOMALIES USING NETFLOW RECORDS

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Authors
Putman, Zachary W.
Subjects
statistical detection
machine learning
NetFlow
denial of service
CRISP-DM
Advisors
Bollmann, Chad A.
Date of Issue
2022-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
NetFlow is a network protocol system that is used to represent an overall summary of computer network conversations. A NetFlow record can convert previously captured packet captures or obtain NetFlow session data in real time. This research examines the use of machine-learning techniques to identify anomalies in NetFlow records and classify malware behavior for further investigation. The intent is to identify low-cost solutions leveraging open-source software capable of deployment on computer hardware of currently in-use data networks. This work seeks to determine whether expert selection of features can improve machine-learning detection algorithm performance and evaluate the trade-offs associated with eliminating redundant or excessive numbers of features. We identify the Random Forest algorithm as the strongest single algorithm across three of four metrics, with our chosen NetFlow features cutting the testing and training times in half while incurring minor reductions in two metrics. The experiment demonstrates that the chosen NetFlow features are sufficiently discriminative to detect attacks with a success rate higher than 94%.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering (ECE)
Organization
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NPS Report Number
Sponsors
NCWDG
Funder
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Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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