PROBLEM-ORIENTED SYSTEM FOR MONITORING AND RESPONDING TO MULTIVECTOR ATTACKS IN A DECENTRALIZED INTERNET OF THINGS ENVIRONMENT

Abstract

Objective: to enhance the effectiveness of monitoring and responding to multivector attacks in a decentralized Internet of Things (IoT) environment by integrating federated learning, deep autoencoders, and the distributed IOTA ledger. The priorities include accurate attack detection, minimizing false positives, reducing response time, and preserving data privacy. Method: a problem-oriented system was developed, combining local monitoring on IoT nodes with autoencoders for anomaly detection, federated learning using the FedAvg algorithm for collective model updates, and decentralized alert dissemination via the distributed IOTA ledger. The system implements secure exchange of model parameters, digital message signing, and asynchronous response through a publish/subscribe network. Results: experimental studies on the real N-BaIoT dataset simulating multivector attacks demonstrated high detection accuracy (approximately 95%), achieving an F1-score above 94%, with false positive rates around 4%. The system's response time did not exceed 5 seconds, significantly improving operational reaction to attacks. Federated learning provided steady improvement in model quality considering data distribution and heterogeneity. The architecture proved scalable, fault-tolerant, and capable of effectively detecting complex threats across multiple system levels. Practical value: the solution is implementable in industrial IoT, smart cities, and medical networks to enhance cybersecurity while maintaining privacy and reducing network load. Scientific novelty: the study presents a comprehensive synthesis of federated learning, deep autoencoders, and distributed ledger technology for effective monitoring of multivector attacks in decentralized IoT environments. The proposed approach combines the advantages of distributed learning and blockchain mechanisms to achieve high adaptability, accuracy, and security in rapidly growing and diverse IoT infrastructures