Publication Date
Fall 2020
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Fabio Di Troia
Third Advisor
Thomas Austin
Keywords
Malware Classification, LSTM, biLSTM, CNNs
Abstract
Signature and anomaly based detection have long been quintessential techniques used in malware detection. However, these techniques have become increasingly ineffective as malware becomes more complex. Researchers have therefore turned to deep learning to construct better performing models. In this project, we create four different long-short term memory (LSTM) models and train each model to classify malware by family type. Our data consists of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP) such as word embedding and bidirection LSTMs (biLSTM). We also use convolutional neural networks (CNN). We found that our model consisting of word embedding, biLSTMs and CNN layers performed the best in classifying malware.
Recommended Citation
Dang, Dennis, "Malware Classification Using LSTMs" (2020). Master's Projects. 963.
DOI: https://doi.org/10.31979/etd.c5r3-dsye
https://scholarworks.sjsu.edu/etd_projects/963
