Deep Learning Empowered Phishing URL Detection an Exhaustive Approach

SubashiniKsuba.pooja@gmail.com

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Deep Learning Empowered Phishing URL Detection an Exhaustive Approach

SubashiniKsuba.pooja@gmail.com ^*

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Abstract

Cybercriminals continually exploit users' vulnerabilities deceptive URLs through phishing attacks are a significant threat to both individuals and organizations. Cybercriminals regularly use phishing to trick users giving them permission to use corporate networks and digital files. Faster Recurrent Convolutional Neural Network (FRCNN) has been proposed to automatically identify phishing websites. However, there are certain drawbacks to its approach: (1) When the URL is converted into a characteristic matrix, there is a storage restriction, making it impossible to gather the embedding vector of new phrases to the actual data of sensitive characters; (2) it is also impossible to acquire the URL's long-distance dependent characteristic. Based on existing system, hybrid model Bidirectional Long Short Term Memory (Bi-LSTM) and FRCNN proposed to identify the phishing attack. The proposed system enables to obtain URL long-distance dependent characteristics by combining two current URL division approaches. Phishing websites can be quickly and accurately identified based on their URLs using the Extreme Gradient Boosting (XGBOOST) and Naïve Bayes Method. According to experimental findings, this approach can produce high F1 values, recall rates and accuracy levels.

Keywords

Machine Learning Phishing Attack Detection Deep Learning Artificial Intelligence Naïve Bayes XGBOOST

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, SubashiniKsuba.pooja@gmail.com. "Deep Learning Empowered Phishing URL Detection an Exhaustive Approach." American Journal of Business and Operations Research, vol. , no. , , pp. . DOI:

, S. (). Deep Learning Empowered Phishing URL Detection an Exhaustive Approach. American Journal of Business and Operations Research, (), . DOI:

, SubashiniKsuba.pooja@gmail.com. "Deep Learning Empowered Phishing URL Detection an Exhaustive Approach." American Journal of Business and Operations Research , no. (): . DOI:

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