ADAPTIVE RECOMMENDATION MODEL FOR CYBERSECURITY MANAGEMENT IN IOT ENVIRONMENTS

Abstract

Recommendation systems for decision support in Internet of Things (IoT) environments play a key role in the development of effective intelligent solutions, providing adaptive cybersecurity management and resource optimization for IoT platforms. Special attention is given to the characteristics of IoT data, which are generated from numerous sources such as sensors, communication devices, and other infrastructure elements, resulting in large volumes of heterogeneous data. This study investigates modern recommendation methods, including collaborative filtering, content-based approaches, deep neural networks, and ensemble learning, as well as their effectiveness in real-world IoT environments. Particular focus is placed on challenges related to algorithm scalability, performance, and energy optimization in low-power devices. The aim of this research is to develop an adaptive recommendation model to support decision-making in IoT environments with a high level of cybersecurity. To achieve this, the study analyzes recent scientific research and solutions in the field of IoT and recommendation systems, examines contemporary platforms implementing similar approaches, identifies technologies and methods suitable for building a recommendation system, and develops a personalized decision support model. The work presents the integration of machine learning methods, big data analytics, and intelligent information processing to ensure adaptive responses to cyber threats. The results demonstrate the potential of combining indicators of compromise, intelligent recommendation models, and threat prediction algorithms to enhance the security of IoT ecosystems. The proposed model not only improves threat detection and real-time response but also enables intelligent management processes, automation of operations, and personalized decision support for IoT system administrators and users. The study highlights the scientific and practical significance of integrating adaptive recommendations into IoT cybersecurity, providing a foundation for further development of hybrid solutions utilizing artificial intelligence and big data processing methods.