多语言推特/X中的网络威胁检测：使用机器学习、深度学习和大语言模型的比较分析
Multi-Lingual Cyber Threat Detection in Tweets/X Using ML, DL, and LLM: A Comparative Analysis
AI总结：
本研究比较了ML、DL和LLM在多语言推文网络威胁检测中的效果，发现双LSTM架构在多语言场景中表现最佳。
摘要：
在当今数字时代，由于社交媒体平台如推特（现称X）上虚假信息和有害内容的快速增长，网络威胁检测已成为一个重要研究领域。这些网络威胁通常伪装在推文中，对个人、社区甚至国家都构成重大风险，强调了有效检测系统的重要性。尽管先前的研究已经探索了基于推文的威胁，但大部分工作局限于特定语言、领域或地区，或依赖单一模型方法，限制了它们在多样化现实场景中的适用性。为了解决这些差距，我们的研究专注于使用各种先进模型进行多语言推文网络威胁检测。研究分为三个阶段：（1）我们使用手动和极性标注方法收集并标注了四种语言（英语、中文、俄语和阿拉伯语）的推文数据集，以确保高质量的注释。（2）每个数据集分别使用机器学习（ML）和深度学习（DL）模型进行分析，以评估其在不同语言上的性能。（3）最后，我们将四个数据集合并成一个单一的多语言数据集，并应用深度学习和大语言模型（LLM）架构来评估其在各种语言中识别网络威胁的有效性。我们的结果表明，在机器学习模型中，随机森林（RF）取得了最高性能；然而，双LSTM架构在所有数据集中都超越了其他DL和LLM架构。这些发现突显了双LSTM在多语言网络威胁检测中的有效性。本文的代码可在此链接中找到：https://github.com/Mmurrad/Tweet-Data-Classification.git。
英文摘要：
Cyber threat detection has become an important area of focus in today's digital age due to the growing spread of fake information and harmful content on social media platforms such as Twitter (now 'X'). These cyber threats, often disguised within tweets, pose significant risks to individuals, communities, and even nations, emphasizing the need for effective detection systems. While previous research has explored tweet-based threats, much of the work is limited to specific languages, domains, or locations, or relies on single-model approaches, reducing their applicability to diverse real-world scenarios. To address these gaps, our study focuses on multi-lingual tweet cyber threat detection using a variety of advanced models. The research was conducted in three stages: (1) We collected and labeled tweet datasets in four languages English, Chinese, Russian, and Arabic employing both manual and polarity-based labeling methods to ensure high-quality annotations. (2) Each dataset was analyzed individually using machine learning (ML) and deep learning (DL) models to assess their performance on distinct languages. (3) Finally, we combined all four datasets into a single multi-lingual dataset and applied DL and large language model (LLM) architectures to evaluate their efficacy in identifying cyber threats across various languages. Our results show that among machine learning models, Random Forest (RF) attained the highest performance; however, the Bi-LSTM architecture consistently surpassed other DL and LLM architectures across all datasets. These findings underline the effectiveness of Bi-LSTM in multilingual cyber threat detection. The code for this paper can be found at this link: https://github.com/Mmurrad/Tweet-Data-Classification.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
