Work

Optimized AI-Driven Semantic Web Approach for Enhancing Phishing Detection in E-Commerce Platforms

Year: 2024

Type: article

Abstract: For e-commerce systems, phishing attempts remain a major threat, so sophisticated detection techniques using Semantic Web and artificial intelligence are very necessary. An efficient AI-driven Semantic Web method for phishing detection enhancement is presented in this work. The approach uses the Chi-square feature selection approach along with the Adaptive Differential Evolution with Optional External Archive (JADE) algorithm to optimize the hyperparameters of a Convolutional Neural Network (CNN) model. Having grown up on a large collection of more than 11,000 webpages, the model attained 93% accuracy. Although alternative models sometimes exceeded it in accuracy, the suggested method always showed the lowest loss values throughout all epochs, therefore stressing its stability and efficiency. Comparative study using conventional models confirms its resilience against phishing attacks for protecting e-commerce systems.

Source: International Journal on Semantic Web and Information Systems

DOI: 10.4018/ijswis.359767

Authors: Akshat Gaurav, Kwok Tai Chui, Varsha Arya, Razaz Waheeb Attar, Shavi Bansal, Ahmed Alhomoud, Konstantinos Psannis

Institutions: Ronin Institute, Hong Kong Metropolitan University, Asia University, Lebanese American University, Princess Nourah bint Abdulrahman University, Chandigarh University, University of Petroleum and Energy Studies, Northern Border University, University of Macedonia

Language: English

Topic: Spam and Phishing Detection

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4404408013

FWCI: 11.78

Cites: 21

Cited by: 31

Related to: 10