Adaptive Threat‐Aware Clustering for Enhanced Security in IoT‐Enabled Smart Cities

Wireless sensor networks (WSNs) that are operating in the Internet of Things (IoT) context for smart city deployments function in a constantly evolving threat landscape that a static trust management architecture cannot address effectively or efficiently. This chapter presents a new dynamic clustering protocol, named Adaptive Threat-Aware Clustering (ATAC), that attempts to proactively address the spectrum of attacks on WSNs through the use of machine learning techniques for threat detection and adaptive trust management. ATAC combines an ensemble of Long Short-Term Memory networks and Random Forest classifiers to identify malicious behavior in real time with dynamically tuned trust thresholds based on the threat context, and adds additional hierarchically based security zones to enable cluster reconfiguration initiation preemptively. The convergence of IoT technologies with smart city infrastructures has necessitated robust security mechanisms to combat evolving cyber threats, with machine learning emerging as a pivotal solution for adaptive threat detection and mitigation.