An integrated model based on deep learning classifiers and pre-trained transformer for phishing URL detection

NQ Do, A Selamat, H Fujita, O Krejcar - Future Generation Computer …, 2024 - Elsevier
The unique nature of website URLs has made phishing detection a challenging task. Unlike
natural language, URLs have an unstructured nature with non-linear and sophisticated …

Multiple adversarial domains adaptation approach for mitigating adversarial attacks effects

B Rasheed, A Khan, M Ahmad… - … on Electrical Energy …, 2022 - Wiley Online Library
Although neural networks are near achieving performance similar to humans in many tasks,
they are susceptible to adversarial attacks in the form of a small, intentionally designed …

Detection of malicious URLs using Temporal Convolutional Network and Multi-Head Self-Attention mechanism

NQ Do, A Selamat, O Krejcar, H Fujita - Applied Soft Computing, 2025 - Elsevier
Abstract Natural Language Processing (NLP) and Deep Learning (DL) have achieved
remarkable results in various fields and have also been proven to be effective in detecting …

Boosting adversarial training using robust selective data augmentation

B Rasheed, A Masood Khattak, A Khan… - International Journal of …, 2023 - Springer
Artificial neural networks are currently applied in a wide variety of fields, and they are near to
achieving performance similar to humans in many tasks. Nevertheless, they are vulnerable …

Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks

B Rasheed, M Abdelhamid, A Khan, I Menezes… - IEEE …, 2024 - ieeexplore.ieee.org
Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and
vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate …

Structure Estimation of Adversarial Distributions for Enhancing Model Robustness: A Clustering-Based Approach

B Rasheed, A Khan, A Masood Khattak - Applied Sciences, 2023 - mdpi.com
In this paper, we propose an advanced method for adversarial training that focuses on
leveraging the underlying structure of adversarial perturbation distributions. Unlike …

Malicious URL detection with distributed representation and deep learning

NQ Do, A Selamat, KC Lim… - New Trends in Intelligent …, 2022 - ebooks.iospress.nl
There exist numerous solutions to detect malicious URLs based on Natural Language
Processing and machine learning technologies. However, there is a lack of comparative …

[HTML][HTML] Improving Robustness of Deep Networks Using Cluster-Based Adversarial Training

B Rasheed, A Khan - Russian Law Journal, 2023 - cyberleninka.ru
Deep learning models have been found to be susceptible to adversarial attacks, which limits
their use in security-sensitive applications. One way to enhance the resilience of these …

Phishmatch: A layered approach for effective detection of phishing urls

H Tupsamudre, S Jain, S Lodha - arXiv preprint arXiv:2112.02226, 2021 - arxiv.org
Phishing attacks continue to be a significant threat on the Internet. Prior studies show that it
is possible to determine whether a website is phishing or not just by analyzing its URL more …

Comparative Analysis of Word vs. Character Embedding for Machine Learning Based Detection of Malicious URLs and DGA-Generated Domains

OC Ayodele, SY Yerima - 2023 IEEE 15th International …, 2023 - ieeexplore.ieee.org
This study presents a comprehensive comparative analysis of the effectiveness of word-level
and character-level embeddings in the context of machine learning-based detection of …