We present a blockchain-based traceability framework for pharmaceutical supply chains, equipped with an automated arbitration mechanism. The framework integrates four core components: (i) a blockchain infrastructure that guarantees immutability, transparency and public verifiability of critical supply chain metadata; (ii) a publicly accessible Cryptographically Controlled Append-only Filesystem that enforces cryptographic access control to ensure secure and auditable raw data storage; (iii) a Secure Leader Election protocol that reliably selects an arbitration coordinator under adversarial conditions; and (iv) an automated arbitration protocol that resolves disputes by evaluating evidence drawn from both the blockchain and the publicly accessible filesystem. We formally define the security properties required for the arbitration protocol. Our integrated design establishes a robust foundation for trustworthy, efficient and autonomous governance in pharmaceutical supply chains.

Healthcare systems are increasingly adopting IoT-enabled solutions, from wearables to remote surgery platforms; thus, we can expect enormous cybersecurity and data protection risks. In this paper, we propose a penetration testing framework for healthcare IoT environments to assess a wide range of vulnerabilities from communication, device control, and data integrity perspectives. Abstract State Machine (ASM) modelling is used by the framework to evaluate real-world attack scenarios, and this helps improve security and compliance against HIPAA and GDPR. Gives tangible methods to improve patient safety while protecting confidential health information.

This paper proposes a novel multi-agent framework for penetration testing that aims to enable efficient and adaptive collaboration of specialised agents. This framework uses the Blackboard system for communication between Scout, Attack, and Analysis Agents. The combined efforts of these agents are aimed at assessing the security available on the networks, and a Decision-Making Agent (DMA) controls it all by making crucial decisions using collected information. This leads to a much better scalability and sophistication of security analyses, thus enhancing the penetration testing process. To guarantee the reliability and robustness of our framework, we use the Abstract State Machine (ASM) as a method to develop a formal model expressing this framework. The model developed is then validated and verified against some defined constraints and properties in order to demonstrate safety, free-deadlock, liveness, and reachability of the elaborated framework.

Traditional penetration testing relies on the domain expert knowledge and requires considerable human effort all of which incurs a high cost. In this paper, we propose an automated penetration testing approach based on the belief-desire-intention (BDI) agent model, which is central in the research on agent based processing in that it deals interactively with dynamic, uncertain and complex environments. Penetration testing actions are defined as a series of BDI plans and the BDI reasoning cycle is used to represent the penetration testing process. The model is extensible and new plans can be added, once they have been elicited from the human experts. We report on the results of testing of proof of concept BDI-based penetration testing tool in the simulated environment.