An Integrated AI and Blockchain-based Cybersecurity Framework for Smart Cities IoT Infrastructure

Abstract

The integration of IoT into smart cities exposes serious cyber security risks, which old centralized designs would fail to combat. In this paper, the model suggests a unified system that would use AI and block chain technology to protect smart city IoT systems. A lightweight hybrid CNN-LSTM model is used to detect anomalies in real-time at the edge nodes and federated learning with differential privacy can be used to train collaboratively without exposing raw data. Permissioned block chain layer provides tamper-proof records and decentralized management of trust. A query fragment caching algorithm is a resource-optimal query strategy to block chain queries. CIC-IDS2017 and BoT-IoT datasets analysis show 98.6% detection, 97.5% F1-score, 134.6 ms latency, and 2,615 transactions-per-second, and 41% less energy usage than un cached block chain access. The framework is more effective than the current methods in all measures.