The exponential growth of big data has transformed the digital landscape, enabling large-scale data-driven decision-making across various sectors. However, this advancement has also led to the proliferation of complex network crimes that exploit the vast and distributed nature of big data systems. This paper proposes a strategic framework designed to enhance the security posture of big data infrastructures by addressing the multifaceted challenges posed by emerging network threats. The methodology involves the integration of dynamic threat detection, context-aware policy enforcement, and real-time anomaly analysis, utilizing a modular architecture that supports scalability and adaptability. Key contributions of this work include the formulation of a layered defense mechanism tailored for heterogeneous data environments, incorporation of predictive intelligence for proactive threat response, and a comparative evaluation against traditional security solutions. Experimental results demonstrate improved detection accuracy and reduced response latency, confirming the effectiveness of the proposed framework. The findings underscore the necessity for a holistic approach that not only safeguards data integrity and privacy but also aligns with the operational demands of high-throughput big data ecosystems. The implications of this research extend to the design of resilient cybersecurity architectures capable of evolving in parallel with the rapidly shifting threat landscape, ultimately supporting safer digital infrastructures in critical domains.

Keywords: Big Data Security, Network Crime Prevention, Strategic Framework, Cybersecurity Architecture, Anomaly Detection, Threat Intelligence