Publication Date
1-1-2021
Document Type
Conference Proceeding
Publication Title
ICISSP 2021 - Proceedings of the 7th International Conference on Information Systems Security and Privacy
DOI
10.5220/0010378007430752
First Page
743
Last Page
752
Abstract
Signature and anomaly based techniques are the quintessential approaches to malware detection. However, these techniques have become increasingly ineffective as malware has become more sophisticated and complex. Researchers have therefore turned to deep learning to construct better performing model. In this paper, we create four different long-short term memory (LSTM) based models and train each to classify malware samples from 20 families. Our features consist of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP), including word embedding and bidirection LSTMs (biLSTM), and we also use convolutional neural networks (CNN). We find that a model consisting of word embedding, biLSTMs, and CNN layers performs best in our malware classification experiments.
Keywords
BiLSTM, CNN, Deep Learning, LSTM, Machine Learning, Malware
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Computer Science
Recommended Citation
Dennis Dang, Fabio Di Troia, and Mark Stamp. "Malware classification using long short-term memory models" ICISSP 2021 - Proceedings of the 7th International Conference on Information Systems Security and Privacy (2021): 743-752. https://doi.org/10.5220/0010378007430752
