A Practical Evaluation of Intrusion Detection in IoT Networks Using Random Forest and Network Intrusion Detection Dataset

In this paper, we evaluate the effectiveness of the Random Forest algorithm in detecting intrusions within IoT environments through features selection analysis. We utilize the Network Intrusion Detection dataset, which simulates various intrusions in a military network environment, to train and test our model. The Random Forest classifier is adopted due to its robustness, scalability, and ability to handle complex data structures. Our results demonstrate that the model achieves high accuracy and efficiency in identifying both known and unknown attacks in IoT traffic. This study provides practical insights into deploying machine learning models for securing IoT infrastructures and highlights the challenges and opportunities in this domain.