Enhancement of Text Recognizing Exploitation in Phishing Websites using
LSTM in Comparison with CNN based on Improving the Accuracy Rate

Shaik Yakub Pasha

doi:10.36948/ijfmr.2023.v05i05.7703

Enhancement of Text Recognizing Exploitation in Phishing Websites using LSTM in Comparison with CNN based on Improving the Accuracy Rate

Author(s)	Shaik Yakub Pasha
Country	India
Abstract	The objective of the work is to predict the accuracy of phishing websites based on exploitation of text recognition using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). To achieve accuracy, a novel np.random function was used. Method and Materials : Accuracy and Loss are performed with DATA dataset from the keras library. The total sample size is 20. The two groups Convolutional Neural Network (N=10) and Long Short Memory (N=10). Result : The result proved that Convolutional Neural Network (CNN) with better accuracy of 97.3% than Long Short Term Memory (LSTM) accuracy of 93.2%. Finally CNN appears significantly better than LSTM. The two algorithms CNN and LSTM are statistically satisfied with the independent sample T-Test value (p<0.001) with a confidence level of 95%. Conclusion : Detecting the phishing website significantly seems to be better in CNN (Std.Error Mean = .0632) than LSTM (Std.Error Mean = .0678).
Keywords	Phishing Websites Detection, Deep Learning, Convolutional Neural Network, Long Short Term Memory, Novel np.random function.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-10-26
Cite This	Enhancement of Text Recognizing Exploitation in Phishing Websites using LSTM in Comparison with CNN based on Improving the Accuracy Rate - Shaik Yakub Pasha - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.7703
DOI	https://doi.org/10.36948/ijfmr.2023.v05i05.7703
Short DOI	https://doi.org/gszvsd

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Enhancement of Text Recognizing Exploitation in Phishing Websites using LSTM in Comparison with CNN based on Improving the Accuracy Rate

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