HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction
Year: 2023
Type: article
Abstract: Phishing techniques are the most frequently used threat by attackers to deceive Internet users and obtain sensitive victim information, such as login credentials and credit card numbers. So, it is important for users to know the legitimate website to avoid the traps of fake websites. However, it is difficult for lay users to distinguish legitimate websites, considering that phishing techniques are always developing from time to time. Therefore, a legitimate website detection system is an easy way for users to avoid phishing websites. To address this problem, we present a hybrid deep learning model by combining a convolution neural network and long short-term memory (HCNN-LSTM). A one-dimensional CNN with a LSTM network shared estimation of all sublayers, then implements the proposed model in the benchmark dataset for phishing prediction, which consists of 11430 URLs with 87 attributes extracted of which 56 parameters are selected from URL structure and syntax. The HCNN-LSTM model was successful in binary classification with accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, 95.00%, successively outperforming the CNN and LSTM. Thus, the results show that our proposed model is a competitive new model for the legitimate web prediction tasks.
Source: Journal of Web Engineering
Authors: Candra Zonyfar, Jung-Been Lee, Jeong-Dong Kim
Institution: Sun Moon University
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Sun Moon University
OpenAlex ID: W4390079679