Blockchain-based incentivization of information flows using fuzzy logic

This thesis addresses the problem of information flow management in IIoT environments, focusing on the assessment of risks arising from illegal or suspicious data exchanges. Initially, the theoretical background of IIoT systems, access control mechanisms, information flow control principles and blockchain technologies is presented. Special emphasis is placed on the unique characteristics of industrial infrastructures, including strict requirements for reliability, availability and resilience against cyber threats. Subsequently, the INFFLOW-RT methodology is examined as a real-time risk-based approach for detecting illegal information flows in IIoT systems. The methodology models industrial environments as dependency graphs and evaluates transactions using descriptors such as data severity, operation type and legality. In addition, it considers risk propagation across multi-order dependencies and the structural importance of nodes, enabling both localized and system-wide risk assessment. Building upon this analysis, the thesis introduces INFFLOW-RT+, a risk-adaptive incentive framework that translates information flow risk assessments into proportional incentive and enforcement actions. The framework employs fuzzy logic to manage uncertainty and gradual behavioral changes, while leveraging blockchain technology and smart contracts to ensure decentralized, transparent and tamper-resistant enforcement. Through this design, secure behavior becomes the most beneficial long-term strategy for participating nodes. Although the proposed framework has not been deployed or experimentally evaluated in real industrial environments, illustrative scenarios are presented to demonstrate its potential effectiveness. These scenarios highlight how linking risk analysis with incentive mechanisms can promote compliance, self-regulation and long-term resilience in IIoT infrastructures. Overall, this thesis contributes to risk-aware and incentive-driven security management in IIoT systems by proposing an integrated and adaptive approach.