Date of Award
12-2023
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Nathan B. Gaw, PhD
Abstract
This thesis addresses challenges with detecting attacks on computer networks within a Federated Learning (FL) framework, when labeled instances are few. We explore the integration of active learning (AL) and semi-supervised learning (SSL). AL efficiently uses data that would otherwise be wasted or require substantial time for labeling. SSL provides capacity to train models that have a limited amount of labeled data, by utilizing additional unlabeled data that is available. We show how FL combined with AL or SSL can realize a detection system that adapts and trains quickly to new networks, reducing the total amount of data labeling needed. This work contributes to more secure network security.
AFIT Designator
AFIT-ENS-MS-23-D-009
Recommended Citation
Sauer, Matthew D. R., "Federated Active Learning for Network Intrusion Detection" (2023). Theses and Dissertations. 7677.
https://scholar.afit.edu/etd/7677
Comments
A 12-month embargo was observed for this thesis.
Distribution Statement A. Approved for public release; PA case number on file.