Enhancing industrial cybersecurity efficiency through the integration of neural networks and ai-based threat detection technologies

In this study, the impact of integrating neural networks and artificial intelligence technologies on the efficiency of cybersecurity systems in digital industrial environments was analyzed. It was examined how advanced AI-based approaches influence the accuracy of cyber threat detection and the speed of response in complex cyber-physical systems. It was investigated the limitations of traditional rule-based and signature-driven cybersecurity mechanisms under conditions of increasing data volumes and system interconnectivity. It was identified the key advantages of neural network architectures, including convolutional neural networks, long short-term memory models, and transformer-based approaches, in detecting anomalous behavior within industrial networks. It was studied the ability of AI-driven systems to process heterogeneous data streams originating from network traffic, system logs, and industrial control systems. It was determined that the integration of artificial intelligence significantly reduces false-positive rates and detection latency while improving overall system resilience. It was established that AI-enhanced cybersecurity frameworks enable a transition from reactive to proactive protection strategies. It was formed a unified analytical framework for evaluating cybersecurity performance before and after AI implementation. It was proposed a scalable approach for deploying intelligent cybersecurity solutions capable of adapting to evolving threat scenarios in digitally transforming industrial infrastructures.