Countermeasure Security Risks Management in the Internet of Things Based on Fuzzy Logic Inference

Abstract

Systems based on the concept of 'Internet of Things' (IoT) are known for multi-tiered architecture, variety and a great number of energy constrained 'things', the influence of new types of attacks, the incompleteness and ambiguity of their parameters. For these reasons, risk management in IoT could be improved by application of fuzzy data processing. The paper considers the main approaches to the construction of intelligent methods and algorithms of information security risk assessment and management for IoT. Mathematical models for security risk assessment in IoT are proposed and investigated. In relation to the concept of multi-agent network control, the Mamdani fuzzy inference procedures for risk assessment and management are developed. Procedures for fuzzy clustering, classification and ranking of security threats are outlined. The experimental results show high stability of the developed security risks management algorithms to uncertainties of input variables.