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Research on Website Phishing Detection Based on LSTM RNN
38
Citations
10
References
2020
Year
Natural Language ProcessingLong-term DependenciesSpam FilteringNew Detection SystemData TimingMachine LearningData ScienceEngineeringSequence ModellingInformation ForensicsWebsite Phishing DetectionDeep LearningPhishingRecurrent Neural NetworkText Mining
In order to effectively detect phishing attacks, this paper designed a new detection system for phishing websites using LSTM Recurrent Neural Networks (RNN). LSTM has the advantage of capturing data timing and long-term dependencies. LSTM has strong learning ability, can automatically learn data characterization without manual extraction of complex features, and has strong potential in the face of complex high-dimensional massive data. Experimental results show that this model approach the accuracy of 99.1%, is higher than that of other neural network algorithms.
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