Vulnerability assessment and federated intrusion detection of Air Taxi enabled smart cities

Abstract

A Sustainable Smart City (SSC) has become the largest industrial sector that encompasses advanced cognitive, information, and computing methods for providing better big data handling to enhance the quality of life and urban operations with optimized resource utilization. Unmanned Aerial Vehicles (UAVs) are used as Air Taxis (ATs) in SSC, bypassing traffic congestion. However, the integration of ATs in SSC is very challenging, seeking high safety and security for the passengers. Such an autonomous system carries vital information, becoming vulnerable to furtive False Data Injection Attacks (FDIA), which have not received considerable research attention heretofore. To address the problem, this article establishes a model-free FDIA employing a recurrent deterministic policy gradient to exploit the data integrity of AT’s sensors, leading to collisions. Thereafter, a decentralized threat detection model, a deep optimized attentive federated aggregation learning is deployed, to counteract the threat and protect the data privacy of each AT. The suggested method is parallelizable and can accurately detect an unobservable FDIA in a distributed manner. The simulation findings show that the proposed attack–defense model investigates in-depth system’s vulnerabilities and tackles the risk with a higher detection rate and reduced computing cost while maintaining privacy preservation that surpasses modern approaches.