Version 1
: Received: 2 December 2023 / Approved: 4 December 2023 / Online: 4 December 2023 (06:58:59 CET)
How to cite:
Almeida, G.; Vasconcelos, F. Analyzing Data Theft Ransomware Traffic Patterns Using BERT. Preprints2023, 2023120158. https://doi.org/10.20944/preprints202312.0158.v1
Almeida, G.; Vasconcelos, F. Analyzing Data Theft Ransomware Traffic Patterns Using BERT. Preprints 2023, 2023120158. https://doi.org/10.20944/preprints202312.0158.v1
Almeida, G.; Vasconcelos, F. Analyzing Data Theft Ransomware Traffic Patterns Using BERT. Preprints2023, 2023120158. https://doi.org/10.20944/preprints202312.0158.v1
APA Style
Almeida, G., & Vasconcelos, F. (2023). Analyzing Data Theft Ransomware Traffic Patterns Using BERT. Preprints. https://doi.org/10.20944/preprints202312.0158.v1
Chicago/Turabian Style
Almeida, G. and Felipe Vasconcelos. 2023 "Analyzing Data Theft Ransomware Traffic Patterns Using BERT" Preprints. https://doi.org/10.20944/preprints202312.0158.v1
Abstract
This research looks into the evolving dynamics of ransomware, shifting from conventional encryption-based attacks to sophisticated data exfiltration strategies. Employing the Bidirectional Encoder Representations from Transformers (BERT) model, the study analyzes network traffic patterns to detect ransomware activities, offering new insights into their covert operations. The findings emphasize the need for advanced AI tools in cybersecurity, highlighting the significance of adapting and innovating defense strategies to counter the changing landscape of ransomware threats. The study contributes to a deeper understanding of ransomware evolution and underscores the importance of integrating AI in cybersecurity practices.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
