FDDDA: False data detection and data aggregation protocol in wireless sensor networks

Abstract

This research paper investigates false data injection attacks, which represent a critical threat to wireless sensor networks because their operational limitations and security weaknesses reduce network reliability, particularly in hierarchical data aggregation systems. The limitations of WSNs make them vulnerable to multiple attacks, which include wormhole attacks, Sybil attacks, and malicious data manipulation and fabricated reports that distort aggregated data accuracy. The authors used systematic analysis methods to investigate security vulnerabilities in hierarchical aggregation systems. They studied methods that can identify real data changes through aggregation while detecting unauthorised data changes via injection attacks. The system uses false data detection methods to secure data aggregation processes, which protect the entire system from data breaches that might compromise data integrity. The results show that attackers who control data aggregation systems can manipulate aggregated data, which results in base stations and end users receiving false information that jeopardizes network safety and disrupts their ability to make choices. The research proves that organizations need to use security and aggregation solutions that operate together to protect their systems from current threats. It highlights the necessity of implementing an integrated security framework that combines robust detection and aggregation strategies to enhance reliability, improve resilience against adversarial attacks, and ensure trustworthy data dissemination in resource-constrained WSN environments.