Intrusion Detection System Using Machine Learning And Deep Learning

The rapid growth of the Industrial Internet of Things (IIoT) has seen network security become increasingly paramount due to the sophistication and frequency of cyber-attack techniques. This paper proposes an IDPS for IIoT contexts that leverages a hybridized machine learning framework. The proposed system is aimed at improving threat detection accuracy and response time through Stacking Classifier framework integration with XGBoost. The performance of the model was evaluated and validated using the NSL-KDD dataset available on Kaggle. Various existing approaches, such as Convolutional Neural Networks (CNN), show promise but are highly susceptible to overfitting and struggle with generalization to different attack patterns. Our hybrid framework, on the other hand, efficiently harnesses the merits of classifiers to enhance detection rates for various types of intrusions. The performance evaluations showed that the proposed model significantly outperforms existing state-of-the-art deep learning models regarding accuracy, precision, recall, and F1-score. This work presents a scalable and efficient solution for real-time intrusion detection and prevention in IIoT infrastructures to ameliorate security vulnerabilities and preserve critical industrial operations.

Keywords: Intrusion Detection and Prevention System (IDPS), Industrial Internet of Things (IIoT), Hybrid Machine Learning, Stacking Classifier, XGBoost, NSL-KDD Dataset, Cyber Security, Network Intrusion Detection