Real-Time Anomaly Detection in IoT Networks with Random Forests and Bayesian Optimization

The increasing prevalence of Internet of Things (IoT) devices has led to a heightened focus on the security and integrity of IoT networks. The present research paper offers a thorough examination of the application of machine learning algorithms for real-time anomaly detection in IoT networks. In this study, we assess the effectiveness of Random Forests, XGBoost, and AdaBoost algorithms in anomaly detection. We employ Bayesian Optimization techniques to fine-tune the hyperparameters of these algorithms and evaluate their performance. In order to evaluate the performance of the algorithms, we utilize the NSL-KDD dataset, which is a well-established benchmark dataset commonly used for detecting intrusions in network traffic. The objective of our study is to assess the precision of these algorithms as well as their applicability for real-time detection in IoT environments. The experimental findings demonstrate that Random Forests exhibit superior performance compared to XGBoost and AdaBoost algorithms, achieving a notable accuracy rate of 99.28% in the detection of network anomalies. The remarkable performance can be ascribed to the collective nature of Random Forests, which amalgamates the capabilities of numerous decision trees and proficiently addresses the issue of overfitting, rendering it highly suitable for detecting anomalies in IoT networks. In addition, Bayesian Optimization plays a crucial role in optimizing the hyperparameters of these algorithms, thereby improving their overall performance and resilience. The significance of parameter optimization in attaining optimal outcomes in anomaly detection tasks is emphasized by our findings. In summary, this study emphasizes the efficacy of Random Forests in the context of real-time anomaly detection within IoT networks, thereby demonstrating their capacity to improve the security and dependability of IoT ecosystems. Furthermore, the utilization of Bayesian Optimization as a technique for tuning hyperparameters highlights its significance in attaining enhanced performance across various machine learning algorithms. The aforementioned observations make a valuable addition to the expanding pool of knowledge focused on enhancing the robustness of IoT networks in the face of emerging security vulnerabilities.