Optimizing Safety In IoT Water Plants: From Critical Importance To Innovative Solutions

Abstract

Cybersecurity of IoT systems in water management is a growing concern due to increasing digital threats. This research focuses on the development of a novel approach that combines dynamic trust management and anomaly detection systems, with a focus on the use of neural networks. The goal is to create a holistic solution that can anticipate and minimize potential threats, thereby reducing the risk of malicious intrusions. The methodology used involves assigning dynamic trust levels to system entities, allowing for immediate response to anomalous behavior. By integrating this with neural network-based anomaly detection systems, the approach enables early detection of unusual patterns, thereby enhancing system security. The results show effective proactive response to attacks, reduced false positives, and early detection of threats. The approach also enables automated incident response and post-incident analysis, strengthening defenses against future attempts. This research stands out for its ability to anticipate threats and its practical application in securing critical infrastructure, particularly in the water sector, to protect essential services.