Beyond Traditional Learning

Abstract

This study explores the efficacy of the bidirectional encoder representations from transformers (BERT) model in the domain of Android malware detection, comparing its performance against traditional machine learning models such as convolutional neural networks (CNNs) and long short-term memory (LSTMs). Employing a comprehensive methodology, the research utilizes two significant datasets, the Drebin dataset and the CIC AndMal2017 dataset, known for their extensive collection of Android malware and benign applications. The models are evaluated based on accuracy, precision, recall, and F1 score. Additionally, the study addresses the challenge of concept drift in malware detection by incorporating active learning techniques to adapt to evolving malware patterns. The results indicate that BERT outperforms traditional models, demonstrating higher accuracy and adaptability, primarily due to its advanced natural language processing capabilities. This study contributes to the field of cybersecurity and NLP.