Detection of DoS Attacks in IoT Networks Using Machine Learning and Multi-Objective Optimization (2024)
Given the characteristics of IoT devices and the growing trend in their number, the security of these devices is critical. In particular, this paper will focus on Denial of Service (DoS) attacks, which seek to overload a service or device by using a machine to render the device inactive. Two machine learning models will be proposed: one from Random Forest, which has an F1 score of 0.99985 and an inference time of 0.457026 seconds for almost 500,000 records, and another from XGBoost, with an F1 score of 0.998989 and an inference time of 0.325767 seconds for the same 500,000 records. According to the methodologies explained, these models were the most suitable to meet the need for security in IoT devices.