Bi-LSTM神经网络方法检测和识别网络威胁、网络跟踪和Twitter中的极端主义推文

A. K, R. O, J. D, S. S
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引用次数: 0

摘要

网络钓鱼攻击是网络威胁之一,受害者会得到危险的url。当你与这些网站互动时,一个窃取凭证的过程就开始了。此外,最近几天,恐怖主义和极端主义推文的传播以及网络跟踪行动有所增加。随着技术的进步,这可以通过机器学习方法和人工智能来解决,通过开发模型和进行自动推文识别。网络威胁、网络跟踪和极端主义评论都可以使用这种实时算法。从Kaggle获得的数据集作为模型的输入,并使用基于twitter数据集的Bi-LSTM方法进行训练。该算法具有优异的性能分数,总准确率为93%,F1分数为95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bi-LSTM Neural Network Approach to Detect and Recognize Cyberthreats, Cyberstalking and Extremist Tweets in Twitter
Phishing attacks, in which victims are handed dangerous URLs, are among the cyberthreats. When you engage with these sites, a process of credential stealing begins. Furthermore, there has been an increase in the transmission of terrorist and extremist tweets, as well as cyberstalking operations, in recent days. As technology advances this can be addressed with machine learning approaches and artificial intelligence by developing models and conducting automated tweet identification. Cyberthreats, cyberstalking, and extremist comments are anticipated using this live algorithm. The dataset obtained from Kaggle is given as input to the model and are trained using the Bi-LSTM method based on a twitter dataset. The algorithm has outstanding performance scores, with a total accuracy of 93% and F1 score of 95%.
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