New Path of Data Science to Ensure Network Information Security in the Age of Big Data

Abstract

This paper explores innovative approaches to ensuring network information security through data science in the big data era. It
analyzes challenges including explosive data growth, intelligent cyberattack tactics, balancing privacy protection with data utilization, and the
limitations of traditional security frameworks. The study highlights core applications of data science in cybersecurity, such as machine learning-driven anomaly detection, graph analytics for identifying complex attack chains, natural language processing in threat intelligence mining,
and predictive analytics for building proactive defense systems. Key technological breakthroughs are introduced, including federated learning
for privacy-preserving collaborative defense, knowledge graphs for threat intelligence systems, edge computing enabling real-time security responses, and blockchain technology ensuring tamper-proof security logs. The paper further examines industry-specific applications: intelligent
upgrades of anti-fraud systems in finance, situational awareness platforms for critical infrastructure, security solutions for government data
openness, and IoT device cluster protection practices. Finally, it outlines future trends including quantum computings impact on encryption
systems and countermeasures, evolving AI security defense strategies, compliance solutions for cross-border data flows, and the development
of security talent cultivation and technological innovation ecosystems.