ﻻ يوجد ملخص باللغة العربية
The proliferation of digitization and complexity of connectivity in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate the functionality and security of critical infrastructures. In this regard, Digital Twins (DTs) are revolutionizing the CPSs. Driven by asset-centric data, DTs are virtual replicas of physical systems that mirror every facet of a product or process and can provide actionable insights through monitoring, optimization, and prediction. Furthermore, replication and simulation modes in DTs can prevent and detect security flaws in the CPS without obstructing the ongoing operations of the live system. However, such benefits of DTs are based on an assumption about data trust, integrity, and security. Data trustworthiness is considered to be more critical when it comes to the integration and interoperability of multiple components or sub-components among different DTs owned by multiple stakeholders to provide an aggregated view of the complex physical system. Moreover, analyzing the huge volume of data for creating actionable insights in real-time is another critical requirement that demands automation. This article focuses on securing CPSs by integrating Artificial Intelligence (AI) and blockchain for intelligent and trusted DTs. We envision an AI-aided blockchain-based DT framework that can ensure anomaly prevention and detection in addition to responding against novel attack vectors in parallel with the normal ongoing operations of the live systems. We discuss the applicability of the proposed framework for the automotive industry as a CPS use case. Finally, we identify challenges that impede the implementation of intelligence-driven architectures in CPS.
Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external threat knowledge provide
Industrial processes rely on sensory data for decision-making processes, risk assessment, and performance evaluation. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the dissemination of trustworthy
The salient features of blockchain, such as decentralisation and transparency, have allowed the development of Decentralised Trust and Reputation Management Systems (DTRMS), which mainly aim to quantitatively assess the trustworthiness of the network
Log-based cyber threat hunting has emerged as an important solution to counter sophisticated cyber attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external knowledge about th
Cyber-physical systems (CPS) are interconnected architectures that employ analog, digital, and communication resources for their interaction with the physical environment. CPS are the backbone of enterprise, industrial, and critical infrastructure. T