Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid

arXiv:2405.03620v1 Announce Type: new
Abstract: As cyber threats and malware attacks increasingly alarm both individuals and businesses, the urgency for proactive malware countermeasures intensifies. This has driven a rising interest in automated machine learning solutions. Transformers, a cutting-edge category of attention-based deep learning methods, have demonstrated remarkable success. In this paper, we present BERTroid, an innovative malware detection model built on the BERT architecture. Overall, BERTroid emerged as a promising solution for combating Android malware. Its ability to outperform state-of-the-art …

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