Year: 2020
Type: dissertation
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.
Author: Dennis Dang
Institution: San Jose State University
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3171242319