Literature Review on Phishing Website Detection Using Deep Learning

Literature Review on Phishing Website Detection Using Deep Learning

Akshatha A P1, Chaithra R1, Madhura S1  , Chandana C Sagar1 , Hiriyanna G S2

* 1UG students, Dept. of CS&E , JNNCE, Shivamogga, India.

* 2Asst. Prof ., Dept. of CS&E , JNNCE, Shivamogga, India.

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Abstract - With the escalating threat of phishing attacks on the internet, the need for effective and efficient methods to identify phishing websites has become paramount. This study explores the application of Deep Learning (DL) techniques for the automated detection of phishing websites. Leveraging the power of neural networks, we analyze various features such as URL structure, content, and visual elements to develop a robust model. The proposed deep learning model exhibits high accuracy in distinguishing between legitimate and phishing websites, demonstrating its capability to adapt to evolving phishing techniques. The training process involves a diverse dataset of labeled examples. Experimental results on benchmark datasets showcase the model's superiority over traditional methods, marking a significant stride in the ongoing battle against cyber threats. The study contributes to the development of proactive measures to safeguard users against the pervasive and evolving nature of phishing attacks in the digital landscape.

Key Words:  Deep Learning, Phishing detection, CNN, Neural Network.