Dynamic attack pattern-aware intelligent cyber-physical intrusion detection system for internet of things-edge networks

Abstract

The proliferation of Internet of Things (IoT) technologies, coupled with the convergence of edge computing infrastructures, has revolutionized modern cyber-physical systems (CPS). However, the inherently distributed architecture of these systems increases their vulnerability to advanced network-level cyber threats, posing significant challenges to data integrity and system reliability. Traditional machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS) often fall short in identifying evolving attack vectors due to their limited adaptability. To address these limitations, this paper introduces a novel Dynamic Attack Pattern-Aware Improvised Weighted Gradient Boosting (DAPA-IWGB) model designed to enhance real-time threat detection and adaptive response within IoT-edge-enabled CPS environments. The DAPA-IWGB framework synergizes gradient tree boosting with an enhanced loss function handling covariate shift, while incorporating statistical monitoring mechanisms for dynamic covariate shift recognition and continuous learning. Comprehensive experimental validation using two prominent benchmark datasets ToN-IoT and UNSW-NB15 demonstrates the proposed model’s robustness and superior performance, achieving detection accuracies of 99.921% and 99.93%, respectively. Comparative evaluations highlight substantial improvements in detection accuracy, adaptability, and reliability over existing IDS solutions. The results affirm the effectiveness of the DAPA-IWGB model in fortifying the security posture of distributed IoT-based CPS against sophisticated and evolving cyber threats.