Anti-Phishing: A Web Identifier for Spoofed Sites Using Neural Network

Abstract

Phishing attacks persist as a significant threat to cybersecurity, exploiting deceptive websites to illicitly acquire sensitive user information. Conventional anti-phishing techniques often struggle to keep pace with the evolving sophistication of these attacks. An approach for detecting phishing websites using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN) known for its ability to capture sequential dependencies in data.Method leverages the temporal dynamics inherent in website content and user interactions to discern between legitimate and phishing websites and constructed a comprehensive data-set comprising diverse samples of legitimate and phishing websites, ensuring the model's ability to generalize across various attack vectors.LSTM-based model exhibits resilience to adversarial evasion techniques commonly employed by attackers, demonstrating robustness in real-world scenarios and conducted extensive analysis to interpret the learned representations captured by the LSTM network, providing insights into the distinguishing features of phishing websites. Key Words:Phishing, Deep Learning,