A Deep Learning Approach Using Convolutional Neural Networks for Enhanced IoT Security and Governance

This article presents a novel approach to enhancing security and governance in smart cities by leveraging a hybrid **Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)** model tailored for Internet of Things (IoT) systems. With the rapid expansion of IoT devices in urban environments, smart cities face unique challenges in managing real-time data and detecting anomalies to prevent security threats. Traditional methods struggle to capture both spatial and temporal patterns essential for effective threat detection and predictive maintenance in such dynamic settings. Our proposed CNN-LSTM method combines CNN's spatial feature extraction capabilities with LSTM's ability to process sequential data, achieving an impressive accuracy of ۹۳% in anomaly detection. This model not only enhances proactive threat response but also enables efficient resource allocation, making it a robust solution for sustainable and secure IoT governance in smart green cities.

A Deep Learning Approach Using Convolutional Neural Networks for Enhanced IoT Security and Governance Keywords:

Convolutional Neural Networks , IoT Security , Smart Cities , Anomaly Detection , Long Short-Term Memory , Deep Learning , Network Analysis