.png){kind=icon}
返回
A data-driven multi-perspective approach to cybersecurity knowledge discovery through topic modelling
.png){kind=icon}
OA
AI
DOI：10.1016/j.aej.2024.07.044.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cybersecurity is crucial for protecting the privacy of digital systems, maintaining economic stability, and ensuring national security. This study presents a comprehensive approach to cybersecurity knowledge discovery through topic modelling, using a multi-perspective analysis of academic and industry sources. The datasets include 15,751 articles from the Web of Science (WoS) database and 5,831 articles from Security Magazine, spanning from 2011 to 2023. We employed BERTopic for topic modelling, UMAP for dimensionality reduction, and HDBSCAN clustering algorithm for grouping and analysing distinct article clusters to uncover latent topics, enhancing the understanding of the evolving cybersecurity landscape. This study found 12 micro-clusters and three macro-clusters, namely technology, smart city and education, from the WoS database and 12 more micro- clusters and four macro-clusters, including organization, public security, governance, and education, from magazines. This study reveals key cybersecurity research and practice trends, such as the increasing focus on malware, ransomware, and cyber-attack mitigation. Additionally, temporal analysis indicates a significant rise in cybersecurity interest around 2020, followed by a diversification of topics. The results highlight the importance of integrating diverse data sources to capture a holistic view of cybersecurity developments. Future work will aim to refine the clustering algorithms to further improve topic extraction and analysis and expand the datasets to include more diverse sources and perspectives. This approach helps discover current cybersecurity trends and provides a foundation for more targeted and effective cybersecurity strategies.
Keyword:
Cyber security
Knowledge discovery
Semantic analysis
Bert
Topic modelling
Natural language processing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.8
论文数:
6.3K
被引数:
2.6W
机构
引用论文
分享收藏Using multi-audio feature fusion for android malware detection使用多音频特征融合进行android恶意软件检测.png)
COMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
分享收藏Investigating ChatGPT and cybersecurity: A perspective on topic modeling and sentiment analysisCOMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
分享收藏Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter非专家用户对网络安全和隐私的看法: 对twitter上在线讨论的分析COMPUTERS & SECURITYOAAI
COMPUTERS & SECURITYIF5.4
OAAI分享收藏Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives用于主动网络安全防御的网络威胁情报挖掘: 调查和新观点
OAAI
OAAI分享收藏
查看更多内容