Advanced Big Data Analytics for Proactive Cyber Threat Mitigation in Large Scale Computer Networks
DOI:
https://doi.org/10.33050/kr1b0a59Keywords:
Cybersecurity, Data, Network, Threat, AnalyticsAbstract
The rapid expansion of digital infrastructure and interconnected systems, large scale computer networks increasingly face sophisticated cyber threats that challenge traditional security mechanisms. The growing volume, velocity, and variety of network data require more advanced analytical approaches capable of detecting and mitigating threats proactively. Over recent years, artificial intelligence and big data technologies have demonstrated strong potential in improving the efficiency and accuracy of cybersecurity systems, particularly in environments characterized by high data complexity and dynamic attack patterns. Motivated by these challenges, this study proposes an advanced big data analytics approach integrated with artificial intelligence techniques to support proactive cyber threat mitigation in large scale computer networks. The proposed method processes large scale network traffic data using intelligent analytical models capable of identifying abnormal behavioral patterns and predicting potential cyber attacks before they fully develop. Experimental simulations using benchmark network datasets indicate that the proposed approach improves detection accuracy, reduces false alarm rates, and enhances the responsiveness of cybersecurity systems when compared with several conventional analytical techniques. The integration of scalable big data processing with adaptive artificial intelligence models also demonstrates strong capability in handling complex and high volume network environments. The findings highlight that advanced big data analytics combined with artificial intelligence can significantly strengthen proactive cyber defense mechanisms in modern computer networks, contributing to more resilient and adaptive cybersecurity infrastructures capable of responding to evolving digital threats.
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