Risk Classification of IP Addresses Using Machine Learning with Weighted Voting Approach - Nobre, Francisco V. J.; Alves, Davi O.; Araujo, Ramon S.; Campos, Gustavo A.; Gomes, Rafael L. (Anais do Latin-American Symposium on Dependable Computing (LADC), 2025)
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.