AI-Based Cyber Threat Intelligence and Prediction for Next Generation Networks (NGN) in Cameroon

The rapid deployment of Next Generation Networks (NGNs) such as 4G/5G, fiber-optics, and software-defined infrastructures in Cameroon, has introduced new cybersecurity challenges, particularly in the face of increasingly sophisticated cyber threats. Traditional intrusion detection and prevention systems lack the scalability, adaptability, and real-time intelligence required to defend against modern attacks such as zero-day exploits, Advanced Persistent Threats (APTs), and insider threats. This paper proposes an AI-based Cyber Threat Intelligence (CTI) and prediction framework specifically designed for the Cameroonian NGN ecosystem. The proposed system leverages machine learning, threat intelligence feeds, and predictive analytics to identify, correlate, and forecast potential cyber threats in real time. It integrates data from diverse sources including telecom infrastructures, national CSIRTs, and open-source threat databases. A prototype implementation using simulated and public NGN traffic datasets demonstrates the system’s effectiveness in early threat detection and attack prediction. The framework aims to support decision-making and proactive cyber defense strategies in Cameroon's telecommunications and public sector environments. Keywords Artificial Intelligence (AI), Cyber Threat Intelligence (CTI), Intrusion Detection, Next Generation Networks (NGN), Cameroon, Threat Prediction, Machine Learning, 5G Security, Federated Learning, Network Security, SIEM Integration, Real-Time Analytics.