DETECTION OF INTRUSIONS IN THE INTERNET OF THINGS USING COMPUTATIONAL INTELLIGENCE TECHNOLOGIES

Abstract

Nowadays the technology of Internet of Things (IoT) is rapidly transforming modern industries by integrating physical objects with digital systems to enable intelligent interaction. However, the growing number of connected devices and the openness of IoT environments pose some cybersecurity issues. Traditional intrusion detection systems (IDS) are often ineffective in IoT networks due to limited device resources, dynamic network topologies, and the increasing number of novel, previously unseen attacks. To improve the efficiency of intrusion detection systems, it is advisable to use an approach based on the use of computational intelligence technologies, including machine learning methods, evolution models, and fuzzy logic algorithms. This paper explores the application of computational intelligence technologies to enhance the effectiveness of intrusion detection in IoT environments. The study substantiates the feasibility of applying computational intelligence technologies to enhance the efficiency of detecting anomalous behavior and unauthorized activities in network traffic. An approach is proposed for designing an intelligent intrusion detection system based on a fuzzy controller capable of adaptively responding to changing environmental conditions and uncertainty in input data. A Mamdani-type fuzzy inference system was developed, including the definition of input and output variables, the construction of a rule base, and the configuration of membership functions. The controller was modeled in the MATLAB environment.  The developed fuzzy controller can serve as a component of intrusion detection systems. The obtained results confirm the feasibility of applying computational intelligence to enhance the reliability of IoT network security. The proposed approach can serve as a foundation for building effective next-generation intrusion detection systems systems to improve the resilience of IoT infrastructures.