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    • Keywords

       

      Deep neural network (DNN); long short-term memory (LSTM); convolution neural network (CNN); recurrent neural network (RNN); phishing; heuristic technique; deep learning

    • Abstract

       

      Phishing is a fraudulent practice and a form of cyber-attack designed and executed with the sole purpose of gathering sensitive information by masquerading the genuine websites. Phishers fool users by replicating the original and genuine contents to reveal personal information such as security number, credit card number, password, etc. There are many anti-phishing techniques such as blacklist- or whitelist-, heuristicfeature-and visual-similarity-based methods proposed as of today. Modern browsers adapt to reduce the chances of users getting trapped into a vicious agenda, but still users fall as prey to phishers and end up revealing their secret information. In a previous work, the authors proposed a machine learning approach based on heuristic features for phishing website detection and achieved an accuracy of 99.5% using 18 features. In this paper, we have proposed novel phishing URL detection models using (a) Deep Neural Network (DNN), (b) Long Short-Term Memory (LSTM) and (c) Convolution Neural Network (CNN) using only 10 features of our earlier work.The proposed technique achieves an accuracy of 99.52% for DNN, 99.57% for LSTM and 99.43% for CNN. The proposed techniques utilize only one third-party service feature, thus making it more robust to failure and increases the speed of phishing detection.

    • Author Affiliations

       

      M SOMESHA1 ALWYN ROSHAN PAIS ROUTHU SRINIVASA RAO VIKRAM SINGH RATHOUR

      1. Information Security Research Lab, National Institute of Technology Karnataka, Surathkal 575025, India
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