Cybersecurity

Real-World Application: Smart City Cybersecurity

The AAIFLF-PPCD model offers a robust solution for securing IoT-assisted sustainable smart cities. By preserving user privacy through Federated Learning and efficiently detecting cyberthreats with high accuracy, it enables critical infrastructure protection. For instance, in a smart grid scenario, early detection of anomalies (e.g., suspicious data injections into smart meters) prevents power outages and data manipulation, ensuring grid stability and citizen safety. Its low processing time allows for real-time threat response, crucial for maintaining uninterrupted city services. The framework's ability to adapt to dynamic threat patterns ensures long-term resilience against evolving cyber risks.

Challenge: Traditional cybersecurity systems struggle with the volume, velocity, and variety of IoT data, often failing to detect sophisticated cyberthreats in real-time while maintaining user privacy. The decentralized nature of IoT deployments further complicates centralized security management.

Solution: AAIFLF-PPCD addresses these challenges by integrating HHO for efficient feature selection, SSAE for accurate cyberthreat classification, and WOA for optimal hyperparameter tuning within a Federated Learning framework. This approach ensures data privacy by keeping raw data local to devices, while collaboratively learning threat patterns. Its superior accuracy (99.47%) and rapid processing (4.51s) enable proactive and effective threat mitigation in dynamic IoT environments.

Outcome: Enhanced security posture for smart city IoT infrastructure, significant reduction in successful cyberattacks, improved data privacy compliance, and real-time anomaly detection capabilities. This leads to greater public trust in smart city initiatives and more resilient urban services, contributing to overall urban sustainability and safety.