Enterprise AI Analysis
Analysis of Research Hotspots and Trends in the Field of Information Security Based on CiteSpace
This study analyzes 598 documents from core Chinese databases (SCI, EI, CSSCI, CSCD) between 2015 and 2025 using CiteSpace. It reveals a significant growth in network information security research, with numerous scholars and collaborative groups. Research focuses include deep study, criminal responsibility in criminal law, data mining for privacy protection, and managing sensitive information leakage in big data contexts. The findings highlight existing research lines and provide a framework for future trends like quantum security, biometric functions, and international cooperative control.
Executive Impact
Key metrics from the research reveal critical trends and opportunities in the information security landscape.
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Documents Analyzed
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Annual Growth Rate (2015-2017)
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Publication Decrease (2021-2025)
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Author Network Density
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Publication Trends
The research volume in network information security showed rapid growth from 2015-2017, coinciding with the 'Cybersecurity Act' promotion, then declined from 2018-2020 due to a shift towards industrial practice, and further decreased from 2021-2025 as traditional areas became saturated and new areas like AI security are still emerging.
Related Chart: Annual Publication Volume in the Field of Network Information Security (Figure 1).
Collaboration Networks
Author collaboration is decentralized with low density (0.0207), showing few high-productivity authors and many independent researchers. Institutional collaboration also shows low density (0.04355), with leading institutions primarily being political and legal universities, indicating a weak network of sustained cooperation.
Related Chart: Author Collaboration Network and Research Institution Collaboration Network (Figures 2 & 3).
Research Hotspots
High-frequency keywords include 'Information Security,' 'Cyberattacks,' 'Big Data,' and 'Neural Networks.' Key technical hotspots are 'blockchain,' 'deep learning,' and 'intrusion detection.' Policy hotspots include 'data security' and 'criminal responsibility,' connecting legal and technical research. Clustering analysis identifies 'Information Security' (with Neural Networks and Big Data) and 'Network-Related Category' (Cybersecurity, Network Information, Cyberattack, Metaverse) as main research directions.
Related Chart: High-Frequency Keywords and Keyword Co-occurrence Graph (Table 2 & Figure 4).
Research Trends & Methods
Recent trends highlight 'Deep Learning,' 'Big Data,' 'Accountability,' and 'Artificial Intelligence.' Research content evolved from 'cyber attacks' to 'network security information' and 'cloud computing'. Future focus includes Metaverse security and preventing confrontational vulnerabilities. Research methods involve technical validation (algorithms, encryption), empirical analysis (case studies, risk models), and political/legal studies (comparative analysis, judicial application). The field is moving from static to dynamic protection and from intelligent to modernization.
Related Chart: Keyword Clustering Analysis and Keyword Burst Word Graph (Figures 5 & 6).
89.5%
Drop in publications from peak to 2025, indicating saturation in traditional areas.
Evolution of Information Security Research
Rapid Growth (2015-2017)
→
Recession & Shift to Practice (2018-2020)
→
Continuous Decline & Saturation (2021-2025)
→
Emergence of New Areas (AI Security, Quantum)
| Feature | Early Focus (2015-2017) | Recent/Future Focus (2020-2025) |
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| Key Keywords |
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| Research Approach |
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Impact of Policy on Research (Cybersecurity Act)
The rapid growth in publications from 2015 to 2017 aligns directly with the public discussion (2016) and official enactment (June 2017) of China's Cybersecurity Act. This highlights the academic community's swift response to critical policy changes, driving a significant increase in related research output. This demonstrates how legislative action can directly stimulate academic interest and publication volume in the field of network information security, especially during critical moments of policy formulation and adoption.
Key Takeaway: Policy initiatives are a strong driver for academic research in information security.
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings your enterprise could achieve by implementing AI solutions based on these research trends.
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Your AI Implementation Roadmap
A structured approach to integrate advanced information security insights into your enterprise operations.
Phase 1: Deep Dive & Assessment
Conduct a comprehensive audit of current network security infrastructure, identifying vulnerabilities and aligning with research hotspots like deep learning for intrusion detection. Evaluate existing data privacy protocols against current trends in criminal liability for data breaches.
Phase 2: Strategy & Pilot Development
Develop a tailored AI security strategy, focusing on emerging trends such as quantum security or advanced biometric functions. Implement a pilot project for a key area, e.g., an AI-powered data mining privacy protection system, ensuring alignment with international cooperative control frameworks.
Phase 3: Scaled Implementation & Integration
Roll out successful pilot projects across the enterprise, integrating new technologies and methodologies into core operations. Establish continuous monitoring and adaptation mechanisms to keep pace with evolving threats and research trends in network information security.
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