MalBERT: Using Transformers for Cybersecurity and Malicious Software Detection
This addresses the need for automated cybersecurity defenses against malware attacks, but it is incremental as it adapts an existing NLP method to a new domain.
The authors tackled the problem of detecting malicious software by applying a BERT-based Transformer model to analyze Android app source code, achieving promising results with high performance in malware classification.
In recent years we have witnessed an increase in cyber threats and malicious software attacks on different platforms with important consequences to persons and businesses. It has become critical to find automated machine learning techniques to proactively defend against malware. Transformers, a category of attention-based deep learning techniques, have recently shown impressive results in solving different tasks mainly related to the field of Natural Language Processing (NLP). In this paper, we propose the use of a Transformers' architecture to automatically detect malicious software. We propose a model based on BERT (Bidirectional Encoder Representations from Transformers) which performs a static analysis on the source code of Android applications using preprocessed features to characterize existing malware and classify it into different representative malware categories. The obtained results are promising and show the high performance obtained by Transformer-based models for malicious software detection.