Machine learning methods can detect Android malware with very high accuracy. However,
these classifiers have an Achilles heel, concept drift: they rapidly become out of date and …

The rapidly evolving nature of Android apps poses a significant challenge to static batch
machine learning algorithms employed in malware detection systems, as they quickly …

This study explores the efficacy of the bidirectional encoder representations from
transformers (BERT) model in the domain of Android malware detection, comparing its …

In recent years, applications of machine learning and artificial intelligence across various
domains are transforming diverse aspects of human life. From autopilot vehicles,[186] and …

This thesis delves into the phenomenon of concept drift, a critical issue in the field of
machine learning where the statistical properties of the target variable, which the model is …

The security community, including both academia and industry, is increasingly adopting
machine learning (ML) for its superior generalizability compared to traditional rule-based …

Tesseract is an open-source framework which enables an unbiased realistic, time-aware
evaluation of machine learning-based malware classification. The Tesseract framework was …