Summary
This chapter provided an introduction to malware and a hands-on blueprint for how it can be detected using transformers. First, we discussed the concepts of malware and the various forms they come in (rootkits, viruses, and worms). We then discussed the attention mechanism and transformer architecture, which are recent advances that have taken the machine learning world by storm. We also looked at BERT, a model that has beat several baselines in tasks such as sentence classification and question-answering. We leveraged BERT for malware detection by fine-tuning a pre-trained model on API call sequence data.
Malware is a pressing problem that places users of phones and computers at great risk. Data scientists and machine learning practitioners who are interested in the security space need to have a strong understanding of how malware works and the architecture of models that can be used for detection. This chapter provided all of the knowledge needed and is a must to master...