A Scalable Security Approach in IoT Networks: Smart Contracts and Anomaly-Based IDS for Gateways Using Hardware Accelerators

Abstract

With the widespread integration of new technologies, IoT devices are becoming increasingly diverse and capable of handling highly complex tasks, compared to previous generations. This evolution has led to demands for a comprehensive security approach across multiple layers of an IoT architecture. This work proposes a scalable security solution from the edge to the cloud, combining Blockchain technology and anomaly-based Intrusion Detection Systems (IDSs). Smart contracts provide a transparent environment for registering and managing IoT devices on the cloud. Specifically, the smart contract includes two authorization levels for managing administrators and IoT devices. Besides, anomaly-based IDSs are deployed at Gateways to detect network attacks. We propose using lightweight machine learning models on FPGA hardware acceleration for Gateways. We have simulated the Blockchain network on the Ganache software, demonstrating that the smart contract effectively manages administrators and devices such that only authorized entities can access the system. The FPGA-based Gateway, which contains pre-trained Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) detection models from the IoT-23 dataset, has been deployed on the Alveo U280 card. The ANN model has achieved the highest processing speed at 20Gbps. The results indicate that integrating Blockchain and anomaly-based IDS significantly enhances scalable security in IoT networks.

Keywords

• Network architecture
• scalable security
• IoT access control
• blockchain
• smart contracts
• neural Networks