Risk Classification of IP Addresses Using Machine Learning with Weighted Voting Approach

• Francisco V. J. Nobre UECE
• Davi O. Alves UECE
• Ramon S. Araujo UECE
• Gustavo A. Campos UECE
• Rafael L. Gomes UECE

Resumo

The growing complexity of cyberattacks demands intelligent and adaptive security solutions, one of which is the detection of malicious IP addresses. This paper presents a novel approach for classifying IP addresses by integrating Machine Learning (ML) with data from multiple public threat intelligence databases, where a novel dataset was built. The proposed solution applies a weighted voting mechanism to enhance interpretability and robustness by combining diverse data sources through a multi-criteria weighting strategy. The experimental results, in a real network environment, indicate that the solution enables scalable and automated risk classification of IP addresses.