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
10.5220/0010377907330742
First Page
733
Last Page
742
Abstract
Malware classification is an important and challenging problem in information security. Modern malware classification techniques rely on machine learning models that can be trained on features such as opcode sequences, API calls, and byte n-grams, among many others. In this research, we consider opcode features. We implement hybrid machine learning techniques, where we engineer feature vectors by training hidden Markov models—a technique that we refer to as HMM2Vec—and Word2Vec embeddings on these opcode sequences. The resulting HMM2Vec and Word2Vec embedding vectors are then used as features for classification algorithms. Specifically, we consider support vector machine (SVM), k-nearest neighbor (k-NN), random forest (RF), and convolutional neural network (CNN) classifiers. We conduct substantial experiments over a variety of malware families. Our experiments extend well beyond any previous related work in this field.
Keywords
CNN, HMM2Vec, Machine Learning, Malware, Word2Vec
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Computer Science
Recommended Citation
Aparna Sunil Kale, Fabio Di Troia, and Mark Stamp. "Malware classification with word embedding features" ICISSP 2021 - Proceedings of the 7th International Conference on Information Systems Security and Privacy (2021): 733-742. https://doi.org/10.5220/0010377907330742
