A Hybrid Intrusion Detection System for Security of Edge-Based IIoT

Abstract

Emergence of cloud computing and IoT technology in healthcare, telecommunications and Industry 4.0 (IIoT), has revolutionized most of the daily services. But this development has also made the aspect of security to be more advanced and complicated. IIoT system security is one of the primary concerns of any industry and researchers. IDS have also materialized as a key part of identifying malicious activity and in attempts to further enhance the security of the IIoT networks. IDS are highly adopted in detecting the real time attacks and making secure decisions. This study proposes a machine learning base intrusion detection system comprises of PCA and XGBoost for edge-based IIoT. The framework combines the techniques of misuse and anomaly detection. It employs Principal Component Analysis (PCA) for dimensionality reduction of the features and Extreme Gradient Boosting (XGBoost) as the intrinsic classifier. PCA makes training faster whereas XGBoost makes detection more accurate The system is evaluated using NSL-KDD and Bot-IoT benchmarks. On NSL-KDD, It obtained detection rate of 98.5%, accuracy of 99.2% and false alarm of 2.6%. It recorded 98.3% accuracy, 97.7% detection rate and 2.8 % false alarm rate on Bot-IoT. These findings indicate that the suggested framework is superior to current IDS models.