Smart tags are compact electronic devices affixed to or embedded into objects to facilitate identification, monitoring, and data exchange. Consequently, secure authentication of these tags is a crucial issue, as objects must reliably verify their identity before sharing sensitive information with other entities. The application of Physical Unclonable Functions (PUF) as a device's “digital fingerprint” has attracted significant attention, yet existing PUF-based authentication methods exhibit security vulnerabilities, either due to the authentication protocol itself or the limited reliability of the PUF technology used. Moreover, there has been a considerable focus on the software aspect, often overlooking the critical role of hardware design, which can become a target for attacks aimed at compromising the device's identity or act as a hindrance in the manufacturing process. In light of these points, this paper introduces an identification tag architecture that leverages PUF technology, focusing on authentication. This architecture features a straightforward but efficient authentication protocol, underpinned by a new and highly stable PUF model. The overall architecture encompasses particular hardware implementation aspects that significantly simplify the tag's enrollment phase and minimize vulnerabilities to attacks. The paper also describes a prototype of this identification tag and provide detailed insights into its application.

The security of Industrial Control Systems (ICSs) is critical to ensuring the safety of industrial processes and personnel. The rapid adoption of Industrial Internet of Things (IIoT) technologies has expanded system functionality but also increased the attack surface, exposing ICSs to a growing range of cyber threats. Honeypots provide a means to detect and analyze such threats by emulating target systems and capturing attacker behavior. However, traditional ICS honeypots, often limited to software-based simulations of a single Programmable Logic Controller (PLC), lack the realism required to engage sophisticated adversaries. In this work, we introduce a modular honeynet framework named ICSLure. The framework has been designed to emulate realistic ICS environments. Our approach integrates physical PLCs interacting with live data sources via industrial protocols such as Modbus and Profinet RTU, along with virtualized network components including routers, switches, and Remote Terminal Units (RTUs). The system incorporates comprehensive monitoring capabilities to collect detailed logs of attacker interactions. We demonstrate that our framework enables coherent and high-fidelity emulation of real-world industrial plants. This high-interaction environment significantly enhances the quality of threat data collected and supports advanced analysis of ICS-specific attack strategies, contributing to more effective detection and mitigation techniques.

The security of Industrial Control Systems (ICSs) is critical to ensuring the safety of industrial processes and personnel. The rapid adoption of Industrial Internet of Things (IIoT) technologies has expanded system functionality but also increased the attack surface, exposing ICSs to a growing range of cyber threats. Honeypots provide a means to detect and analyze such threats by emulating target systems and capturing attacker behavior. However, traditional ICS honeypots, often limited to software-based simulations of a single Programmable Logic Controller (PLC), lack the realism needed to deceive sophisticated adversaries. In this work, we introduce a modular honeynet framework named ICSLure.The framework has been designed to emulate realistic ICS environments. Our approach integrates physical PLCs interacting with live data sources via industrial protocols such as Modbus/Profinet RTU, along with virtualized network components including routers, switches, and Remote Terminal Units (RTUs). The system incorporates comprehensive monitoring capabilities to collect detailed logs of attacker interactions. We demonstrate that our framework enables coherent and high-fidelity emulation of real-world industrial plants. This high-interaction environment significantly enhances the quality of threat data collected and supports advanced analysis of ICS-specific attack strategies, contributing to more effective detection and mitigation techniques.