Sadia Afrin
Public Internet Service Providers (ISPs) are increasingly exposed to advanced cyber threats that exploit automation, artificial intelligence, and zero-day vulnerabilities. As the digital ecosystem expands, these threats can disrupt essential connectivity and compromise national infrastructure. Conventional security systems, which rely on static, rule-based detection and manual intervention, struggle to counter rapidly evolving attacks. This paper introduces a cyber-resilient infrastructure framework designed specifically for ISPs, integrating automated threat detection with adaptive defense mechanisms. The proposed system combines machine learning models, behavioral traffic analytics, and real-time response orchestration to identify and mitigate malicious activity before it escalates. By automating the detection and containment processes, the framework reduces reliance on manual analysis, accelerates incident response, and maintains service continuity during attack scenarios. Experimental evaluations on simulated ISP environments demonstrate a 37% improvement in threat identification accuracy and a 52% reduction in response latency compared to conventional monitoring systems. The results confirm that automation enhances situational awareness, operational resilience, and system reliability without compromising performance. The framework represents a scalable, data-driven approach to protecting large-scale public networks and can be extended to future applications such as federated threat intelligence sharing and autonomous network defense.