IMPLEMENTATION OF DEEP LEARNING FOR DETECTING PHISHING ATTACKS ON WEBSITES WITH COMBINATION OF CNN AND LSTM - Raihan, Ahmad; Fadhli, Mohammad; Lindawati, Lindawati (Informatika, Universitas Jenderal Soedirman, 2024)
Phishing attacks represent significant cyber threats to internet users, particularly on websites. These attacks are conducted by perpetrators seeking to acquire victims' data by impersonating legitimate websites. To address this threat, a solution is proposed using deep learning with a combined algorithm of convolutional neural network and long short-term memory. The research methodology included data collection comprising phishing and legitimate website links, pre-processing through tokenization, padding, and labeling, and splitting data into training and testing sets. The models were then trained, and grid search was employed to identify the optimal hyperparameters for each algorithm. The algorithm’s performance was calculated by accuracy, precision, recall, and F1-score metrics. The outcomes indicated that using the combination algorithm achieved 95.63% accuracy, 94.60% precision, 96.81% recall, and 95.78% f1-score. This paper concludes the proposed algorithm is effective in detecting phishing attacks on websites.