iCDRET: A Dynamic Cyber Risk Estimation Technique for Intelligent Cyber-physical Systems in PCS

To detect cyberattacks and assess risks in cyber-physical systems used in pharmaceutical treatment, this study presents a two-tiered approach that combines machine learning and Internet of Things (IoT) security. By prioritizing risk-based responses and facilitating real-time threat mitigation, it improves system resilience.

Drug delivery, patient data management, and healthcare efficiency have all been greatly improved by the incorporation of cyber-physical systems (CPS) into pharmaceutical care services. However, because digital and physical infrastructures are now interconnected, this development has created serious cybersecurity risks. Strong detection and mitigation procedures are necessary because cyberattacks have the potential to compromise patient safety, data integrity, and service reliability. The necessity for a specific cybersecurity architecture for pharmaceutical CPS is highlighted by the fact that current security solutions frequently fall short in addressing real-time threat detection and risk assessment.

To detect and eliminate cyber threats instantly, the suggested method makes use of sophisticated machine learning models, intrusion detection systems, and Internet of Things security strategies. A risk-estimation system that assesses attacks according to impact, detectability, and risk estimation factor (REF) is included in a two-layered strategy. To evaluate the performance of the proposed method in comparison with existing security frameworks, simulations were conducted. Analysis is performed on important variables such as system resilience, risk quantification, and detection accuracy.

The results of the simulation show that the suggested method improves the accuracy of threat identification and offers a methodical framework for risk evaluation. The strategy demonstrates increased accuracy in detecting cyberattacks and prioritizing mitigation measures when compared to current approaches. By accurately estimating the intensity of an assault, the risk estimation approach guarantees preventative security measures.

A comprehensive answer to the changing cybersecurity issues in pharmaceutical CPS is provided by the layered architecture that combines machine learning and IoT security. The suggested approach facilitates informed, risk-based decision-making for mitigation in addition to improving real-time threat detection. This proactive strategy protects sensitive patient data and helps avoid service interruptions. Nevertheless, the study has certain drawbacks, such as reliance on the caliber of training data and the requirement for frequent model upgrades in order to accommodate new threats. Future studies might concentrate on combining adaptive models and federated learning to improve system privacy and adaptability even more.

A unique cybersecurity architecture for intelligent CPS in pharmaceutical care is presented in this paper. The method improves system resilience, protects patient data, and guarantees the dependable functioning of pharmaceutical services against changing cyber threats by combining real-time threat detection with risk assessment.