Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Fabio Di Troia

Second Advisor

Thomas Austin

Third Advisor

Katerina Potika


adaptive machine learning, adaptive random forest (ARF), RF, kNNADWIN, kNN, OnlineBoosting, AdaBoost, SVM, Word2Vec, malware classification, concept drift, stream data, opcodes


Malware classification is the process of distinguishing malware samples into categories of malware families that it is associated with and remains a critical step in the process of mitigating malware-related threats. In recent years, machine learning techniques have emerged as a powerful tool for such malware classification tasks. In this study, we explore the application of adaptive machine learning models to malware classification in order to analyze and determine how they compare in performance to similar but non-adaptive algorithms. The results achieved in this study share insight into the strengths and limitations of adaptive learning models when applied towards malware classification. Overall, we see that adaptive machine learning models are dull in comparison to their non-adaptive counterparts in performance and accuracy results.

Available for download on Friday, May 23, 2025