Work

AI-Powered Detection of Spear Phishing and Digital Arrest Attacks in E-Commerce

Year: 2025

Type: article

Abstract: Phishing and digital arrest attacks pose significant threats to e-commerce security, necessitating advanced AI-driven detection mechanisms. In this context, this study proposed a hybrid deep learning model integrating BERT for feature extraction, Atrous Spatial Pyramid Pooling (ASPP) for multi-scale feature refinement, and a CNN-based classification network. The BERT encoder captures semantic and contextual features, while ASPP enhances spatial hierarchies using dilated convolutions. A CNN classifier with batch normalization and ReLU activation ensures robust classification. Extensive experiments on a Kaggle dataset demonstrate superior performance, achieving 97.81% accuracy, 98% precision, and an AUC of 0.9972, outperforming GRU, LSTM, RNN, and Transformer models.

Source: International Journal on Semantic Web and Information Systems

DOI: 10.4018/ijswis.382475

Authors: Vincent Shin-Hung Pan, Razaz Waheeb Attar, Amal Hassan Alhazmi, Ahmed Alhazmi, Varsha Arya, Ching-Hsien Hsu, Ahmed Alhomoud

Institutions: Chaoyang University of Technology, Princess Nourah bint Abdulrahman University, Umm al-Qura University, Chandigarh University, University of Petroleum and Energy Studies, Hong Kong Metropolitan University, Asia University, Northern Border University

Language: English

Topic: Spam and Phishing Detection

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4411759937

FWCI: 1.107

Cites: 17

Cited by: 1

Related to: 10