<![CDATA[The rapid expansion of large-scale computer networks and the exponential growth of big data have significantly increased the complexity and frequency of cyber threats, rendering traditional signature-based security mechanisms inadequate for adaptive detection. This study aims to develop a self-learning AI model capable of autonomously identifying evolving attack patterns and anomalous behaviors in large-scale networks without relying exclusively on pre-labeled datasets. The proposed framework integrates deep neural architectures, incremental learning, and behavior-based traffic analysis to enable continuous adaptation to dynamic threat environments while ensuring computational efficiency and scalability. The model was trained and evaluated using realistic network traffic datasets simulating distributed attacks, zero-day exploits, and advanced persistent threats across heterogeneous environments. Experimental findings demonstrate that the self-learning approach enhances detection accuracy, reduces false positives, and accelerates response times compared to conventional intrusion detection systems. In addition, the combination of deep neural architectures with incremental learning and scalable data processing further strengthens model robustness and adaptability in complex and evolving networks. The results indicate that integrating adaptive AI into cybersecurity frameworks enhances proactive defense capabilities, improves resilience in large-scale computer networks, and provides a scalable, intelligent solution for next-generation threat detection systems. This study highlights the practical relevance of combining AI, big data analytics, and cybersecurity strategies to support intelligent, adaptive security solutions capable of addressing emerging threats, minimizing operational risks, and fostering robust network protection in increasingly complex digital infrastructures.]]>
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