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 …
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 …
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 …
Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate …
In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike …
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 …
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 …
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 …
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 …