A robust and dynamic malware detection and classification model using behavioral-based analysis and BERT technique.

ABSTRACT: Malware classification is a challenging task due to the constantly evolving nature of malicious software. Traditional signature-based methods and static analysis often fail to detect sophisticated threats, making behavior-based analysis crucial. This study proposes a malware detection model that analyzes the behavior of executable files (.exe) to classify them as malware. The model submits the file to VirusTotal, where it runs in a secure environment to monitor actions such as file modifications, registry changes, or network connections. To enhance detection accuracy, the BERT model is applied to extract key features from these behavior logs. After 100 training epochs, the model achieved 92.25% accuracy and an F1-score of 91.22%, demonstrating strong overall performance. Class-wise evaluat

SUBMITTER: Alhazmi AH 

PROVIDER: S-EPMC12410719 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature