Real-Time IoT Traffic Anomaly Detection Using Smart Contract and Machine Learning

Abstract

The Internet of Things (IoT) has transformed various industries by enabling seamless connectivity among smart devices, but its open nature exposes it to security vulnerabilities. Blockchain technology, with its decentralized and immutable properties, offers a promising solution to enhance IoT security. However, existing anomaly detection approaches in IoT networks face limitations such as high false positives, scalability issues, and lack of real-time threat mitigation. To address these challenges, this research integrates the Isolation Forest algorithm with a blockchain-based smart contract for efficient anomaly detection. The Isolation Forest algorithm is used to classify network anomalies by classifying normal and abnormal traffic patterns, while smart contracts detect anomalies in real-time network traffic, ensure data integrity, automate threat responses, and provide tamper-proof logging. Experimental evaluations demonstrate the effectiveness of this approach, achieving improved accuracy of 95 percent along with other measures such as precision, recall, and F1-score also achieving good results compared to other traditional methods. The proposed framework enhances IoT security by reducing false alarms, increasing detection sensitivity, and enabling real-time threat identification, making it a scalable and robust solution for modern IoT environments.