Intruder Detection System for IOT network Using Hyperparameter Optimization Framework

The rapid adoption of the Internet of Things (IoT) due to its ease of use and diversity has prompted serious security concerns about networked devices around the world. This emphasizes the critical necessity for robust Intrusion Detection Systems (IDS) to combat cyber threats. However, the widespread interconnectedness and diversity of IoT devices have posed substantial security challenges, particularly in terms of network infiltration. This study investigates the design and implementation of an IDS for IoT networks utilizing the OPTUNA Hyperparameter Optimization Framework. The proposed IDS seeks to identify and prevent several cyber threats through investigating network traffic and recognizing aberrant patterns that indicate intrusions. This study contributes to the field of IoT security by presenting an effective and extensible ID method that takes advantage of the optimization framework. Additionally, the OPTUNA framework is utilized to optimize classifier performance by fine-tuning hyperparameters and determining the ideal model configuration. These models are evaluated using publicly available NSL-KDD datasets. The results of this study show that OPTUNA hyperparameter optimization can improve classification results significantly. Evaluation using the NSL-KDD dataset, the proposed OPTUNA XGB model has generated the better accuracy performance as 0.9675 while compared to other classification models.