The growth of data volume has changed cybersecurity activities, demanding a higher level of automation. In this new cybersecurity landscape, text mining emerged as an alternative to improve the efficiency of the activities involving unstructured data. This article proposes a Systematic Literature Review ( SLR ) to present the application of text mining in the cybersecurity domain. Using a systematic protocol, we identified 2,196 studies, out of which 83 were summarized. As a contribution, we propose a taxonomy to demonstrate the different activities in the cybersecurity domain supported by text mining. We also detail the strategies evaluated in the application of text mining tasks and the use of neural networks to support activities involving unstructured data. The work also discusses text classification performance aiming its application in real-world solutions. The SLR also highlights open gaps for future research, such as the analysis of non-English content and the intensification in the usage of neural networks.

中文翻译：

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网络安全中的文本挖掘

数据量的增长改变了网络安全活动，要求更高水平的自动化。在这种新的网络安全环境中，文本挖掘成为提高涉及非结构化数据的活动效率的一种替代方法。本文提出一个系统文献综述(单反) 介绍文本挖掘在网络安全领域的应用。使用系统方案，我们确定了 2,196 项研究，其中 83 项进行了总结。作为贡献，我们提出了一个分类法来展示文本挖掘支持的网络安全领域的不同活动。我们还详细介绍了在应用文本挖掘任务和使用神经网络支持涉及非结构化数据的活动中评估的策略。该工作还讨论了文本分类性能，旨在将其应用于现实世界的解决方案。SLR 还突出了未来研究的空白，例如对非英语内容的分析和神经网络使用的强化。