Optimized AI-Driven Semantic Web Approach for Enhancing Phishing Detection in E-Commerce Platforms
Authors: 
Kwok Tai Chui, Konstantinos Psannis, Varsha Arya, Akshat Gaurav, Razaz Waheeb Attar, Shavi Bansal, Ahmed AlhomoudAuthors Info & Claims
Pages 1 - 13
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.
References
[1]
Abebe, M. (2014). Electronic commerce adoption, entrepreneurial orientation and small- and medium- sized enterprise (sme) performance. Journal of Small Business and Enterprise Development, 21(1), 100–116.
[2]
Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing website detection with semantic features based on machine learning clas- sifiers: A comparative study. International Journal on Semantic Web and Information Systems, 18(1), 1–24.
[3]
Bhardwaj, A., & Kaushik, K. (2022). Predictive analytics-based cybersecurity framework for cloud infrastructure. International Journal of Cloud Applications and Computing, 12(1), 1–20.
[4]
Bitaab, M., Cho, H., Oest, A., Lyu, Z., Wang, W., Abraham, J., & Doupé, A. (2023). Beyond phish: Toward detecting fraudulent e-commerce websites at scale. In 2023 ieee symposium on security and privacy (sp).
[5]
Chand, E. (2019). Phishing website detector. https://www.kaggle.com/datasets/ eswarchandt/phishing-website-detector/data. (Accessed: 2024-01-30)
[6]
Costa, J., & Castro, R. (2021). Smes must go online—E-commerce as an escape hatch for resilience and survivability. Journal of Theoretical and Applied Electronic Commerce Research, 16(7), 3043–3062.
[7]
GuoQ.LiZ.AnB.HuiP.HuangJ.ZhangL.ZhaoM. (2019). Securing the deep fraud detector in large-scale e-commerce platform via adversarial machine learning approach. The Web Conference. 10.1145/3308558.3313533
[8]
Gupta, B. B., Tewari, A., Cvitic´, I., Perakovic´, D., & Chang, X. (2022). Artificial intelligence empow- ered emails classifier for internet of things based systems in industry 4.0. Wireless Networks, 28(1), 493–503.
[9]
Gupta, B. B., Yadav, K., Razzak, I., Psannis, K., Castiglione, A., & Chang, X. (2021). A novel approach for phishing urls detection using lexical based machine learning in a real-time environment. Computer Communications, 175, 47–57.
[10]
Hu, B., Gaurav, A., Choi, C., & Almomani, A. (2022). Evaluation and comparative analysis of semantic web-based strategies for enhancing educational system development. International Journal on Semantic Web and Information Systems, 18(1), 1–14.
[11]
Jain, A. K., Gupta, B. B., Kaur, K., Bhutani, P., Alhalabi, W., & Almomani, A. (2022). A content and url analysis-based efficient approach to detect smishing sms in intelligent systems. International Journal of Intelligent Systems, 37(12), 11117–11141.
[12]
Kabango, C., & Romeo, A. (2015). Factors influencing e-commerce development: Implications for the developing countries. International Journal of Innovation and Economic Development, 1(1), 59–66.
[13]
Kinda, T. (2019). E-commerce as a potential new engine for growth in asia. IMF Working Paper, 19(135), 1.
[14]
Li, S., Qin, D., Wu, X., Li, J., Li, B., & Han, W. (2022). False alert detection based on deep learning and machine learning. [IJSWIS]. International Journal on Semantic Web and Information Systems, 18(1), 1–21.
[15]
Megaw, G., & Flowerday, S. V. (2010). Phishing within e-commerce: A trust and confidence game. In 2010 information security for south africa.
[16]
Mohan, N. R., & Kumar, N. P. (2020). Predicting and analysis of phishing attacks and breaches in e-commerce websites. International Journal of Scientific Research in Science, Engineering and Technology, 170–175.
[17]
Nanduri, J., Jia, Y., Oka, A., Beaver, J., & Liu, Y. (2020). Microsoft uses machine learning and optimization to reduce e-commerce fraud. INFORMS Journal on Applied Analytics, 50(1), 64–79. Advance online publication.
[18]
Nguyen, L. A. T., To, B. L., Nguyen, H. K., & Nguyen, M. H. (2013). Detecting phishing web sites: A heuristic url-based approach. In 2013 international conference on advanced technologies for communications (atc 2013).
[19]
Pan, X., Yamaguchi, S., Kageyama, T., & Kamilin, M. H. B. (2022). Machine-learning-based white-hat worm launcher in botnet defense system. International Journal of Software Science and Computational Intelligence, 14(1), 1–14.
[20]
Tembhurne, J. V., Almin, M. M., & Diwan, T. (2022). Mc-dnn: Fake news detection using multi- channel deep neural networks. International Journal on Semantic Web and Information Systems, 18(1), 1–20.
[21]
Vajrobol, V., Gupta, B. B., & Gaurav, A. (2024). Mutual information based logistic regression for phishing url detection. Cyber Security and Applications, 2, 100044.
[22]
Zhang, J., & Sanderson, A. C. (2009). Jade: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13(5), 945–958.
[23]
Zhang, Y., & Qiao, Y. (2020). A detection method for phishing web page using dom-based doc2vec model. CIT. Journal of Computing and Information Technology, 28(1), 19–31.
Index Terms
- Optimized AI-Driven Semantic Web Approach for Enhancing Phishing Detection in E-Commerce Platforms
Index terms have been assigned to the content through auto-classification.
Recommendations
Enhancing Phishing Detection in Semantic Web Systems Using Optimized Deep Learning Models
Phishing detection in Semantic Web systems is crucial to safeguarding users from malicious attacks. In this context, this work presents a deep learning-based phishing attack detection model using MobileBERT for feature extraction and hyperparameter ...
Enterprise Credential Spear-phishing attack detection
Highlights- Detecting spear-phishing email attacks based on sender domains.
- Figuring out that the domain's sender is genuine or forgery.
- Providing a balanced trade-off between detecting targeted attacks and bypassing the genuine emails.
AbstractThe latest report by Kaspersky on email Spam and targeted Phishing attacks, by percentage, highlights the need of an urgent solution. Attachment-driven Spear-phishing struggles to succeed against many email providers’ malware-filtration systems, ...
Graphical abstractDisplay Omitted
An Anti-Phishing Approach that Uses Training Intervention for Phishing Websites Detection
ITNG '09: Proceedings of the 2009 Sixth International Conference on Information Technology: New GenerationsPhishing scams have become a problem for online banking and e-commerce users. This paper proposes and evaluates a novel anti-Phishing approach that uses training intervention for Phishing websites detection (APTIPWD). The proposed approach helps users ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2024.
Publisher
IGI Global
United States
Publication History
Published: 13 December 2024
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 07 Jan 2025