TOWARDS ROBUST LEARNING USING DIAMETRICAL RISK MINIMIZATION FOR NETWORK INTRUSION DETECTION
Authors
McCollum, Kelson J.
Subjects
network intrusion detection
machine learning
robust learning
diametrical risk minimization
machine learning
robust learning
diametrical risk minimization
Advisors
Royset, Johannes O.
Date of Issue
2023-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Currently, deep neural networks (DNNs) show great promise in the detection of malicious network traffic at machine speed. However, these networks are typically trained using Empirical Risk Minimization (ERM), which is not robust to misclassified or altered training data. We propose applying Diametrical Risk Minimization (DRM), which is shown to lead to more robust optimization solutions, to train DNNs to classify malicious network traffic. Using two different network traffic datasets, we find that when state-of-the-art DNNs are trained on partially mislabeled data, utilizing DRM results in higher accuracy compared to equivalent models trained with ERM in 13 of 20 cases examined, with ERM being more accurate in only 5 of the 20 cases. More importantly, when models are tested against previously unseen cyber-attack types, models trained with DRM correctly identify the previously unseen cyber-attacks more often. Of the 46 cases we examine, models trained with DRM show better performance compared to models trained with ERM in 25 cases and equal performance in an additional 10 cases. We show that these DNNs are computationally tractable to deploy in real-time on edge computing systems utilizing commercial-off-the-shelf hardware.
Type
Thesis
Description
Series/Report No
NPS Outstanding Theses and Dissertations
Department
Operations Research (OR)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
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.