A Conceptual and Simulation-Based Framework for AI-Driven Network Security Automation: Case Study CAMTEL

The increasing complexity of multi-vendor network infrastructures presents substantial challenges in ensuring robust cybersecurity. Conventional network defense mechanisms are often inadequate to counter the evolving sophistication and dynamism of contemporary cyber threats. Furthermore, the proliferation of 5G networks, Internet of Things (IoT) devices, and cloud computing technologies has accelerated digital transformation, driving economic growth and connectivity while simultaneously expanding the attack surface. As cyberattacks become more advanced, maintaining network security and data integrity has become increasingly difficult. This paper investigates the application of Artificial Intelligence (AI) in enhancing network security by addressing critical challenges in predictive threat detection, real-time anomaly response, and network optimization. AI techniques demonstrate exceptional effectiveness in these domains, enabling proactive identification and mitigation of threats while improving overall network performance. Experimental results indicate that AI-driven security systems achieve up to 92% accuracy in cyber threat detection, reduce average incident response time to under 1.5 minutes, and enhance bandwidth allocation efficiency by 35% during peak traffic periods. The study further introduces a conceptual framework for AI-based network security automation, integrating machine learning, predictive analytics, and natural language processing within network monitoring tools. This model enables autonomous threat detection, response, and prevention, contributing to more resilient and adaptive cybersecurity infrastructures.