TY - GEN
T1 - Optimized Deep Learning Based Phishing Email Detection Using BERT and Hill Climbing Algorithm
AU - Gaurav, Akshat
AU - Gupta, Brij B.
AU - Castiglione, Arcangelo
AU - Bansal, Shavi
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Prevention of cybersecurity risks in contemporary communication systems depends on phishing email detection. This work presents an optimal deep learning method using the Hill Climbing (HC) algorithm for hyperparameter optimization and BERT for feature extraction to improve phishing detection. Using a Kaggle dataset, the model was trained with balanced precision, recall, and F1-scores for phishing and safe emails with a 95% accuracy. In terms of decreased loss and improved generalization, a comparative study encompassing GRU, LSTM, RNN, Logistic Regression, and SVM showed the suggested method’s excellence. The results highlight how well feature extraction combined with optimization methods could help to identify phishing emails in practical environments.
AB - Prevention of cybersecurity risks in contemporary communication systems depends on phishing email detection. This work presents an optimal deep learning method using the Hill Climbing (HC) algorithm for hyperparameter optimization and BERT for feature extraction to improve phishing detection. Using a Kaggle dataset, the model was trained with balanced precision, recall, and F1-scores for phishing and safe emails with a 95% accuracy. In terms of decreased loss and improved generalization, a comparative study encompassing GRU, LSTM, RNN, Logistic Regression, and SVM showed the suggested method’s excellence. The results highlight how well feature extraction combined with optimization methods could help to identify phishing emails in practical environments.
KW - BERT
KW - Hill Climbing Algorithm
KW - Phishing Detection
UR - https://www.scopus.com/pages/publications/105008659281
U2 - 10.1007/978-981-96-6389-7_23
DO - 10.1007/978-981-96-6389-7_23
M3 - Conference contribution
AN - SCOPUS:105008659281
SN - 9789819663880
T3 - Lecture Notes in Computer Science
SP - 258
EP - 269
BT - Computational Data and Social Networks - 13th International Conference, CSoNet 2024, Proceedings
A2 - Kertesz, Janos
A2 - Li, Bo
A2 - Supnithi, Thepchai
A2 - Takhom, Akkharawoot
T2 - 13th International Conference on Computational Data and Social Networks, CSoNet 2024
Y2 - 16 December 2024 through 18 December 2024
ER -