Abstract
Phishing is a deceitful trick of cyber-attack designed and implemented by scammers and hackers with purpose of stealing personal data by impersonating the original websites. Phishing is like fishing in a lake wherein the users are very conveniently be fooled by scammers (phishers) by impersonating original websites and contents to leak their valuable personal and professional data. Currently a lot of anti phishing tools and techniques are being applied to detect and nullify the phishing cyber threat viz, heuristic feature, blacklist or white list and visual similarity-based approaches. In this research paper, we have anticipated robust and novel anti-phishing models via (I) Long Short-Term Memory (LSTM), (II) Deep-Neural Network (DNN) and (III) Convolution-Neural Network (CNN) using 10 features. The anticipated model achieves an accuracy of 98.67% for LSTM, 96.33% for DNN and 97.23% for CNN. The proposed techniques are highly efficient and robust which increases the phishing detection manifold.
Published Version
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