Blockchain-Enabled Artificial Intelligence System for Real-Time Threat Detection in IoT Networks

Abstract

The attack surface for data manipulation, unauthorized access, and distributed denial of service (DDoS) is expanding due to the continuous expansion of connected Internet of Things (IoT) devices. The Centralized security solutions are hazards to privacy and single points of failure when managing the Internet of Things' size and variety. This article suggests a Blockchain-Enabled Artificial Intelligence System (BCAIS) for Real-time Threat Detection in IoT Networks to provide decentralized trust, improved data integrity, and transparent security event monitoring. To enable real-time anomaly detection and automated reaction, the suggested model combines machine learning-based threat identification with blockchain-based security event monitoring. The methodology is designed to ensure tamper-resistant security event records while identifying known and undiscovered dangers across heterogeneous IoT domains. The model's performance is evaluated using accuracy, precision, recall, F1-score, and detection latency. This technology enhances cyber resilience in critical sectors including manufacturing, energy, and infrastructure, where maintaining operational continuity and safety depends on timely and dependable attack responses by enabling quick, comprehensible, and low-latency intrusion detection directly on edge devices.