Implementation of unsupervised Transformers models to predict DDoS attacks

• Grant number: 25/01627-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: April 01, 2025
• End date: March 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Michele Nogueira Lima
• Grantee: Amanda Silveira Barbosa
• Host Institution: Instituto de Ciências Exatas (ICEx). Universidade Federal de Minas Gerais (UFMG). Ministério da Educação (Brasil). Belo Horizonte , SP, Brazil
• Associated research grant: 18/23098-0 - MENTORED: from modeling to experimentation - predicting and detecting DDoS and zero-day attacks, AP.TEM

Abstract

The prediction of DDoS attacks is evolving over time, improving the detection of cyber threats and making systems protection more efficient. However, this prediction still faces challenges, mainly due to the growing complexity and sophistication of attacks. Given this scenario, it is essential to develop more adaptable solutions capable of effectively anticipating attacks in different contexts. Therefore, this scientific initiation proposes the research and implementation of unsupervised Transformer models for predicting DDoS attacks. This approach aims to overcome existing limitations by leveraging the ability of Transformers to identify hidden patterns without the need for labeled data. The goal is to contribute to the evolution of attack prediction strategies within the scope of the MENTORED project, strengthening cybersecurity through advanced machine learning techniques.