Distributed Cybersecurity Preemptiveness on Industrial IoT Network Infrastructures
Edidiong Elijah Akpan, Ugochukwu Okwudili Matthew, Hope Ayokunle Oladele, Prisca Ijeoma Okochi, Oluwatosin Samuel Falebita, Nabeela Temitayo Adebola, Godwin Nse Ebong, Nneoma Andrew-Vitalis · 2025
In this paper, the authors introduced a cybersecurity system based on two principles: first, to gain knowledge from the process's typical behavior, as it detects anomalous behavior susceptible to cyberattack and sounds an alarm when it is discovered. A blockchain federated learning technology-based security system provides centralized control for improved security and vulnerability management in distributed and multi-site e-healthcare data warehouse environments. The suggested framework incorporates a blockchain-based federated learning network for detecting Distributed Denial of Service (DDoS) attacks on e-healthcare organizational data warehouse infrastructure, and data obtained from an IoT node is gathered by a Message Queuing Telemetry Transport (MQTT) broker, which then sends the information to the computing platform for analysis. This research paper proposed an innovative multi-agent blockchain based federated learning intrusion detection system (IDS) that employed an attention-based mechanism to detect and classify cyberattacks in addition to defense measures.