Title: Phishing detection model using feline finch optimisation-based LSTM classifier

Authors: Mohd Shoaib; Mohammad Sarosh Umar

Addresses: Department of Computer Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India ' Department of Computer Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India

Abstract: Phishing detection technologies are essential to ensuring that users have a secure online experience by preventing users from falling prey to online fraud, divulging personal information to an attacker, and other risks. To stop e-mail scams, this research employs the FFO-LSTM classifier to identify phishing websites. The FFO method is used to pick and optimise the relevant parameters from many parameters that the LSTM offers. This reduces the computational complexity of the system and improves performance. The LSTM classifier conducts a more thorough examination of the network, which aids in enhancing the effectiveness of phishing detection. The FFO-LSTM classifier achieved exceptional values of 93.16%, 94.59%, and 92.66%, which illustrates the efficiency of the research. The performance metrics are utilised to illustrate the significance of the research.

Keywords: phishing; LSTM classifier; FFO algorithm; SMOTE; synthetic data.

DOI: 10.1504/IJSNET.2023.133249

International Journal of Sensor Networks, 2023 Vol.42 No.4, pp.205 - 220

Received: 13 Jan 2023
Accepted: 21 May 2023
Published online: 03 Sep 2023 *

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