Dissertation/Thesis Abstract

M-AdaBoost-A Based Ensemble System for Network Intrusion Detection
by Zhou, Ying, Ph.D., The George Washington University, 2021, 128; 28256014
Abstract (Summary)

Network intrusion detection remains a challenging research area as it involves learning from large-scale imbalanced multiclass datasets. While machine learning algorithms have been widely used for network intrusion detection, most standard techniques cannot achieve consistent good performance across multiple classes. In this dissertation, a novel ensemble system was proposed based on the Modified Adaptive Boosting with Area under the curve (M-AdaBoost-A) algorithm to detect network intrusions more effectively. Multiple M-AdaBoost-A-based classifiers were combined into an ensemble by employing various strategies, including particle swarm optimization. To the best of our knowledge, this study is the first to utilize the M-AdaBoost-A algorithm for addressing class imbalance in network intrusion detection. Compared with existing standard techniques, the proposed ensemble system achieved superior performance across multiple classes in both 802.11 wireless intrusion detection and traditional enterprise intrusion detection.

Indexing (document details)
Advisor: Mazzuchi, Thomas A., Sarkani, Shahram
Commitee: Etemadi, Amir , Holzer, Thomas , Blackford, Joseph P.
School: The George Washington University
Department: Systems Engineering
School Location: United States -- District of Columbia
Source: DAI-B 82/6(E), Dissertation Abstracts International
Subjects: Systems science, Computer Engineering, Artificial intelligence
Keywords: Boosting, Class imbalance, Ensemble learning, Machine learning, Network intrusion detection, Particle swarm optimization
Publication Number: 28256014
ISBN: 9798698567899
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