主题识别和精细的演变路径分析网络安全领域的文献
Yanfeng Zhu1, Zheng Li2, Tianyi Li1
1School of Computer and Big Data, Heilongjiang University, Harbin, China.
PloS one
|February 21, 2025
概括
本研究使用LDA2vec和动态主题模型分析了2003-2022年网络安全研究趋势. 该LDA模型有效地绘制了网络安全主题的演变,揭示了复杂的趋势和技术进步与安全威胁之间的平衡.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 数据科学数据科学数据科学
背景情况:
- 确定研究主题的进化对于理解科学领域至关重要.
- 网络安全是一个快速发展的领域,具有复杂的研究环境.
研究的目的:
- 从2003年到2022年,分析网络安全研究主题的演变.
- 在主题演变分析中比较LDA和LDA2vec模型的有效性.
- 揭示网络安全研究发展的趋势和模式.
主要方法:
- 网络科学文学 (2003-2022) 的文本分析.
- 隐藏的迪里克莱特分配 (LDA) 和Word2vec的整合用于主题识别.
- 应用LDA2vec模型用于主题相关性和进化路径构建.
- 使用动态主题模型 (DTM) 进行比较分析.
主要成果:
- 与DTM相比,LDA模型在主题提取和进化路径描述方面表现出更高的适用性和清晰度.
- LDA2vec模型有效地捕获了相邻主题之间的相关性和演变模式.
- 网络安全研究主题表现出复杂性,多样性和偶尔的回溯.
- 在网络安全中,技术进步和安全威胁之间存在动态平衡.
结论:
- 在分析网络安全主题演变方面,LDA模型非常有效.
- 网络安全研究的特点是复杂而多样化的不断发展的主题.
- 该领域在创新和威胁缓解之间保持着持续的相互作用.
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