International Journal of Science, Engineering and TechnologyAuthors: Harisankar R Nair, Duane Chembakassery, Prassanna J
Abstract: The rapid proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including unauthorized access, data breaches, and distributed denial-of-service (DDoS) attacks. Traditional security mechanisms often fall short due to the resource constraints of IoT devices and the evolving nature of cyber threats. This paper presents a novel Machine Learning (ML)-powered anomaly detection system, integrated with Blockchain-Based Trust Management, to enhance IoT security. The proposed system utilizes a Raspberry Pi 4 as an edge device to monitor real-time network traffic, extract relevant features, and employ an Autoencoder/LSTM-based ML model for anomaly detection. When an anomaly is detected, the system triggers an alert, mitigates threats using firewall rules, and logs security events on a private blockchain. A trust score mechanism is implemented via smart contracts to dynamically assess device behavior, enabling automated device quarantine and adaptive security measures. Experimental evaluations demonstrate that the LSTM model achieves an accuracy of 98.85%, with high precision and recall, effectively identifying network anomalies. The blockchain-based trust management framework ensures tamper-proof security event logging while dynamically adjusting device trust scores based on anomaly detection results. The results indicate that this integrated approach enhances both detection accuracy and accountability in IoT networks, making it a viable solution for real-time, decentralized IoT security.
DOI: http://doi.org/10.5281/zenodo.21703358
