Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Hoang, Xuan Dau
Unknown Affiliation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A novel model for detecting web defacement attacks transformer using plain text features Hoang, Xuan Dau; Nguyen, Trong Hung; Pham, Hoang Duy
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp232-240

Abstract

Over the last decade, web defacements and other types of web attacks have been considered serious security threats to web-based services and systems of many enterprises and organizations. A website defacement attack can bring severe repercussions to the website owner, such as immediate discontinuance of the website operations and damage to the owner’s reputation, which may lead to enormous monetary losses. Several solutions and tools for monitoring and detecting web defacements have been designed and developed. Some solutions and tools are limited to static web pages, while others can handle dynamic ones but demand significant computational power. The existing proposals’ other issues are relatively low detection rates and high false alarm rates because many crucial elements of web pages, including embedded code and images are not properly processed. This paper proposes a novel model for detecting web defacements to address these issues. The model is based on the bidirectional long-short term memory (Bi-LSTM) deep learning method using features of the plain text content extracted from web pages. Comprehensive testing on over 96,000 web pages dataset demonstrates that the proposed Bi-LSTM-based web defacement detection model outperforms earlier methods, achieving a 96.04% overall accuracy and a 2.03% false positive rate.
A novel multimodal model for detecting Vietnamese toxic news using PhoBERT and Swin Transformer V2 Le, Ngoc An; Hoang, Xuan Dau; Vu, Xuan Hanh; Ninh, Thi Thu Trang
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1350-1359

Abstract

News articles with fake, toxic or reactionary content are currently posted and spreaded very strongly due to the popularity of the Internet and especially the explosion of social networks and online services in cyberspace. Toxic news, especially reactionary news aimed at Vietnam, such as online articles spreading false information, slandering leaders, inciting destruction of the great national unity bloc, have a great impact on social life because they can spread quickly and have many forms of expression, such as news in the forms of text, images, videos, or a combination of text and images. Due to the seriousness of articles posting fake, toxic or reactionary news in cyberspace, there have been a number of studies in Vietnam and abroad for detection and prevention. However, most of the proposals focus on handling fake and toxic news posted using the English language. Furthermore, due to a large number of online news are posted in the form of images, or text embedded in images and videos, it is very difficult to process these news, leading to a relatively low detection rate. This paper proposes a multimodal model based on the combination of PhoBERT and Swin Transformer V2 for detecting fake and toxic news in both forms of text and images. Comprehensive experiments conducted on a dataset of 8,000 text and image news articles demonstrate that the proposed multimodal model surpasses both individual models and previous approaches, achieving 95% accuracy and 95% F1-score.
Co-Authors Le, Ngoc An Nguyen, Trong Hung Ninh, Thi Thu Trang Pham, Hoang Duy Vu, Xuan Hanh