<![CDATA[The rapid expansion of large scale computer networks driven by cloud infrastructures, Internet of Things environments, and distributed digital services has significantly increased the complexity of cybersecurity threats. Traditional rule based security systems often struggle to detect evolving and previously unseen attacks within high volume network traffic. This study proposes a self learning artificial intelligence approach designed to enhance threat detection capability in large scale computer networks by leveraging adaptive learning mechanisms and large scale network data analysis. The proposed framework integrates machine learning models with big data processing techniques to continuously learn from network traffic patterns, behavioral anomalies, and historical security events. Through automated feature extraction and iterative model refinement, the system dynamically improves its ability to identify malicious activities without relying solely on predefined signatures. This study adopts a qualitative conceptual evaluation approach to examine the proposed self-learning artificial intelligence and big data framework for cybersecurity resilience in distributed computer networks. The evaluation is conducted through literature synthesis, comparative analysis of existing intrusion detection approaches, architectural modeling, and conceptual validation of the proposed framework against key cybersecurity requirements, including adaptability, scalability, continuous learning, and detection coverage for known and unknown threats. The system also shows strong scalability in processing high volume network data while maintaining stable detection performance. These results indicate that integrating self learning artificial intelligence with scalable data processing can strengthen cybersecurity resilience in large scale computer networks and support the development of more adaptive and intelligent network defense mechanisms for future digital infrastructures.]]>
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