Phishing detection system through hybrid machine learning based on URL

A Karim, M Shahroz, K Mustofa, SB Belhaouari… - IEEE …, 2023‏ - ieeexplore.ieee.org
Currently, numerous types of cybercrime are organized through the internet. Hence, this
study mainly focuses on phishing attacks. Although phishing was first used in 1996, it has …

{KnowPhish}: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing {Reference-Based} Phishing Detection

Y Li, C Huang, S Deng, ML Lock, T Cao, N Oo… - 33rd USENIX Security …, 2024‏ - usenix.org
Phishing attacks have inflicted substantial losses on individuals and businesses alike,
necessitating the development of robust and efficient automated phishing detection …

[HTML][HTML] Novel interpretable and robust web-based AI platform for phishing email detection

A Al-Subaiey, M Al-Thani, NA Alam, KF Antora… - Computers and …, 2024‏ - Elsevier
Phishing emails continue to pose a significant threat, causing financial losses and security
breaches. This study addresses limitations in existing research, such as reliance on …

Investigation of phishing susceptibility with explainable artificial intelligence

Z Fan, W Li, KB Laskey, KC Chang - Future Internet, 2024‏ - mdpi.com
Phishing attacks represent a significant and growing threat in the digital world, affecting
individuals and organizations globally. Understanding the various factors that influence …

Multimodal large language models for phishing webpage detection and identification

J Lee, P Lim, B Hooi, DM Divakaran - arxiv preprint arxiv:2408.05941, 2024‏ - arxiv.org
To address the challenging problem of detecting phishing webpages, researchers have
developed numerous solutions, in particular those based on machine learning (ML) …

[HTML][HTML] Искусственный интеллект и кибербезопасность

ДЕ Намиот, ЕА Ильюшин, ИВ Чижов - International Journal of …, 2022‏ - cyberleninka.ru
В этой статье мы рассматриваем связь систем искусственного интеллекта и
кибербезопасности. В современной трактовке, системы искусственного интеллекта …

" Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages

Y Yuan, Q Hao, G Apruzzese, M Conti… - Proceedings of the ACM …, 2024‏ - dl.acm.org
Machine learning based phishing website detectors (ML-PWD) are a critical part of today's
anti-phishing solutions in operation. Unfortunately, ML-PWD are prone to adversarial …

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 …

[HTML][HTML] Beyond the west: Revealing and bridging the gap between Western and Chinese phishing website detection

Y Yuan, G Apruzzese, M Conti - Computers & Security, 2025‏ - Elsevier
Phishing attacks are on the rise, and phishing websites are everywhere, denoting the
brittleness of security mechanisms reliant on blocklists. To cope with this threat, many works …

Attacking logo-based phishing website detectors with adversarial perturbations

J Lee, Z **n, MNP See, K Sabharwal… - … on Research in …, 2023‏ - Springer
Recent times have witnessed the rise of anti-phishing schemes powered by deep learning
(DL). In particular, logo-based phishing detectors rely on DL models from Computer Vision …