Predictive Analysis of Network-Based Attacks by Hybrid Machine Learning Algorithms Utilizing Bayesian Optimization, Logistic Regression, and Random Forest Algorithm

These days, intrusion detection systems are one of the newest trends in society. These technologies serve as a defense against a variety of security breaches, the number of which has been rising recently. The need for adaptive security solutions is pressing since the sorts of attacks that arise are ever-changing. This study aims to enhance the performance of intrusion detection models on the KDD99 and NSL-KDD datasets through advanced optimization techniques. By addressing challenges related to evolving attack strategies and intricate tasks, the research introduces innovative machine learning approaches tailored for intrusion detection, focusing on both binary and multiclass classification scenarios. The study employs a Bayesian Optimization-enhanced Random Forest (BO_RF) algorithm for binary classification and a hybrid Logistic Regression and Random Forest (LR_RF) algorithm for multiclass classification. Our models were implemented and evaluated in a Jupyter Notebook environment using key metrics: Accuracy, Precision, Recall, and F1-Score. For binary classification, eight metrics were assessed, while twenty-six were analyzed for multiclass classification across both datasets. The results demonstrate the effectiveness of the proposed approaches in both classification types, highlighting their potential for robust and adaptable intrusion detection. Theoretical contributions include advancing the understanding of intrusion detection methodologies and the effectiveness of machine learning algorithms in cybersecurity. From a practical perspective, the proposed model can offers a robust and adaptable solution for real-world intrusion detection scenarios, potentially minimizing security breaches and enhancing overall cyber security posture.