Detecting phishing domains using machine learning

S Alnemari, M Alshammari - Applied Sciences, 2023 - mdpi.com
Phishing is an online threat where an attacker impersonates an authentic and trustworthy
organization to obtain sensitive information from a victim. One example of such is trolling …

Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning

MA Tamal, MK Islam, T Bhuiyan, A Sattar… - Frontiers in Computer …, 2024 - frontiersin.org
Introduction The dynamic and sophisticated nature of phishing attacks, coupled with the
relatively weak anti-phishing tools, has made phishing detection a pressing challenge. In …

[HTML][HTML] Phishing websites detection using a novel multipurpose dataset and web technologies features

M Sánchez-Paniagua, E Fidalgo, E Alegre… - Expert Systems with …, 2022 - Elsevier
Phishing attacks are one of the most challenging social engineering cyberattacks due to the
large amount of entities involved in online transactions and services. In these attacks …

[HTML][HTML] The applicability of a hybrid framework for automated phishing detection

RJ van Geest, G Cascavilla, J Hulstijn, N Zannone - Computers & Security, 2024 - Elsevier
Phishing attacks are a critical and escalating cybersecurity threat in the modern digital
landscape. As cybercriminals continually adapt their techniques, automated phishing …

Staying ahead of phishers: a review of recent advances and emerging methodologies in phishing detection

S Kavya, D Sumathi - Artificial Intelligence Review, 2024 - Springer
The escalating threat of phishing attacks poses significant challenges to cybersecurity,
necessitating innovative approaches for detection and mitigation. This paper addresses this …

Detection of adversarial phishing attack using machine learning techniques

KM Sudar, M Rohan, K Vignesh - Sādhanā, 2024 - Springer
The frequency of cyberattacks, particularly phishing attacks, is increasing exponentially
every day. Many users fall victim to clicking on malicious URLs, leading to the exploitation of …

DaE2: Unmasking malicious URLs by leveraging diverse and efficient ensemble machine learning for online security

AE Omolara, M Alawida - Computers & Security, 2025 - Elsevier
Over 5.44 billion people now use the Internet, making it a vital part of daily life, enabling
communication, e-commerce, education, and more. However, this huge Internet connectivity …

Heuristic machine learning approaches for identifying phishing threats across web and email platforms

R Jayaprakash, K Natarajan, JA Daniel… - Frontiers in Artificial …, 2024 - frontiersin.org
Life has become more comfortable in the era of advanced technology in this cutthroat
competitive world. However, there are also emerging harmful technologies that pose a …

PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification

F Castaño, EF Fernañdez, R Alaiz-Rodríguez… - IEEE …, 2023 - ieeexplore.ieee.org
Recent studies have shown that phishers are using phishing kits to deploy phishing attacks
faster, easier and more massive. Detecting phishing kits in deployed websites might help to …

URL based phishing attack detection using BiLSTM-gated highway attention block convolutional neural network

M Nanda, S Goel - Multimedia Tools and Applications, 2024 - Springer
Phishing is an attack that attempts to replicate the official websites of businesses, including
government agencies, financial institutions, e-commerce platforms, and banks. These …