AI-Driven Cyber Threat Detection for Securing National Critical Infrastructure

Abstract

This research explores the application of Artificial Intelligence (AI) in enhancing cyber threat detection mechanisms aimed at protecting national infrastructure. The purpose of the study is to evaluate how AI-driven approaches, particularly machine learning and deep learning techniques, can improve the speed, accuracy, and adaptability of cybersecurity systems in the face of increasingly sophisticated and persistent threats targeting critical sectors such as energy, transportation, water, and communications. The methodology involves a comparative analysis of traditional signature-based detection systems versus AI-enhanced models using real-world datasets and simulated cyber-attack scenarios. The study utilizes supervised and unsupervised learning algorithms, including neural networks and anomaly detection frameworks, to assess performance across detection rate, false positive rate, and response time. Key findings indicate that AI-enhanced systems significantly outperform traditional methods in early detection of zero-day attacks, adaptive threat response, and overall threat landscape analysis. Additionally, AI models demonstrate improved scalability and resilience in handling high-volume, high-velocity network traffic. The research concludes that the integration of AI into national cybersecurity infrastructure provides a transformative capability for proactive defense. However, it also highlights the need for continuous model training, ethical oversight, and hybrid human-AI decision frameworks to mitigate risks such as algorithmic bias and adversarial manipulation.