Real-Time Cyber Threat Detection in Smart Cities Using Artificial Intelligence

This paper proposes an intelligent hybrid framework based on Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks with Particle Swarm Optimization (PSO) to enhance real-time cyber threat detection in smart cities. It tries to address cybersecurity challenges posed by the Internet of Things (IoT) devices in smart cities and the necessity for timely responses to emerging threats. The model incorporates the collection and preprocessing of sequential data from network traffic logs, followed by the design and implementation of an RNN-LSTM model tailored for temporal pattern recognition. PSO is deployed to optimize the model’s hyperparameters when offline, achieving significant improvements in detection accuracy and latency. The results indicate an appropriate detection accuracy of 97.5% and a recall rate of 96.8%, demonstrating the effectiveness of the proposed framework. This research shows the importance of dynamic optimization techniques in adapting to the evolving security landscape of smart cities. It also highlights the critical role of machine learning in safeguarding urban infrastructure and enhancing the resilience of smart city environments against cyber threats.