Simulation and modeling of deep adversarial probabilistic neural network-based intrusion prevention system in cloud computing for smart grid cyber physical systems

Abstract

Smart Grid Cyber Physical Systems (SG-CPS) have a substantial impact on power grid infrastructure upgrading. Nonetheless, due to the sophisticated nature of the infrastructure and the critical demand for resilient intrusion prevention systems, the task of protecting its security against data integrity attacks is a significant challenge. The simulation and assessment of security performance in SG-CPS present substantial hurdles in real-world power grid systems, owing mostly to experimental constraints. This necessitates the development of novel ways to improve distribution chain security. This research introduces a novel approach, employing a Deep Adversarial Probabilistic Neural Network (DAPNN)-based Intrusion prevention system in a cloud environment. Combining Bayesian Probabilistic Neural Networks (BEPNNs) with adversarial training and rule-based decision-making enhances precision and resilience. The major goal of this research is to detect and counteract false data injection (FDI) attacks that have the potential to compromise the integrity of power grid data. This paper proposes a novel methodology for intrusion detection in SG-CPS that combines BEPNNs with adversarial training. The addition of rule-based decision-making improves the system’s precision and resilience. The IEEE 24-bus system provides the foundation for providing data points relevant to normal operating conditions, contingency scenarios, and intentional attacks. The training procedure includes the use of a BEPNN for feature extraction, as well as the use of adversarial training approaches. The intrusion detection system has decision-making logic based on rules. The cloud infrastructure solution used in the study is Microsoft Azure. The results show that the DAPNN-based Intrusion Prevention System is effective in detecting and mitigating FDI attacks in SG-CPS. The system outperforms in terms of accuracy, precision, recall and F-measure, hence improving the security of the power grid infrastructure.