Optimized Intrusion Detection in IoT Environments Using Machine Learning and Nature-Inspired Algorithms

Abstract

As the number of IoT devices rises, there is a growing demand for intrusion detection systems capable of protecting IoT environments. However, due to the complexity and heterogeneity of data from IoT networks, detecting malicious events and securing the network presents a significant challenge. In light of these difficulties, this study proposes an improved machine learning method that employs optimization strategies for intrusion detection in IoT settings. The methodology begins with basic data pre-processing techniques, such as handling missing values and normalizing the dataset for uniformity. Feature selection is carried out using the Cyber Range Ant Optimization Algorithm, ensuring that the algorithm identifies the crucial features for model training. The dataset is then divided into training and testing sets to ensure that the model generalizes well. The Python environment, along with libraries such as scikit-learn and pandas, is used to process and analyze the dataset. The Cyber Ant Opto Boost Algorithm is employed for classification, while Ant Colony Optimization is used to fine-tune hyperparameters in order to improve model accuracy. The performance of the model is evaluated using the testing data. The proposed methodology highlights the use of nature-inspired algorithms in machine learning pipelines and provides a scalable and powerful approach for detecting intrusions in IoT environments that are dynamic and complex. This makes the application both practical and adaptable, representing a significant step toward better optimization in machine learning models.