Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Fabio Di Troia
Second Advisor
Thomas Austin
Third Advisor
Katerina Potika
Keywords
adaptive machine learning, adaptive random forest (ARF), RF, kNNADWIN, kNN, OnlineBoosting, AdaBoost, SVM, Word2Vec, malware classification, concept drift, stream data, opcodes
Abstract
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.
Recommended Citation
Boddukuri, Rashmi, "Analysis and Application of Adaptive ML Algorithms for Malware Classification" (2024). Master's Projects. 1382.
DOI: https://doi.org/10.31979/etd.jqbv-5es5
https://scholarworks.sjsu.edu/etd_projects/1382
