A survey of machine learning-based solutions for phishing website detection

L Tang, QH Mahmoud - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
With the development of the Internet, network security has aroused people's attention. It can
be said that a secure network environment is a basis for the rapid and sound development of …

SoK: a comprehensive reexamination of phishing research from the security perspective

A Das, S Baki, A El Aassal, R Verma… - … Surveys & Tutorials, 2019 - ieeexplore.ieee.org
Phishing and spear phishing are typical examples of masquerade attacks since trust is built
up through impersonation for the attack to succeed. Given the prevalence of these attacks …

Detecting Internet of Things attacks using distributed deep learning

GDLT Parra, P Rad, KKR Choo, N Beebe - Journal of Network and …, 2020 - Elsevier
The reliability of Internet of Things (IoT) connected devices is heavily dependent on the
security model employed to protect user data and prevent devices from engaging in …

Malicious URL detection using machine learning: A survey

D Sahoo, C Liu, SCH Hoi - arXiv preprint arXiv:1701.07179, 2017 - arxiv.org
Malicious URL, aka malicious website, is a common and serious threat to cybersecurity.
Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure …

Phishing website detection based on multidimensional features driven by deep learning

P Yang, G Zhao, P Zeng - IEEE access, 2019 - ieeexplore.ieee.org
As a crime of employing technical means to steal sensitive information of users, phishing is
currently a critical threat facing the Internet, and losses due to phishing are growing steadily …

An effective phishing detection model based on character level convolutional neural network from URL

A Aljofey, Q Jiang, Q Qu, M Huang, JP Niyigena - Electronics, 2020 - mdpi.com
Phishing is the easiest way to use cybercrime with the aim of enticing people to give
accurate information such as account IDs, bank details, and passwords. This type of …

A stacking model using URL and HTML features for phishing webpage detection

Y Li, Z Yang, X Chen, H Yuan, W Liu - Future Generation Computer …, 2019 - Elsevier
In this paper, we present a stacking model to detect phishing webpages using URL and
HTML features. In terms of features, we design lightweight URL and HTML features and …

Classifying phishing URLs using recurrent neural networks

AC Bahnsen, EC Bohorquez, S Villegas… - … on electronic crime …, 2017 - ieeexplore.ieee.org
As the technical skills and costs associated with the deployment of phishing attacks
decrease, we are witnessing an unprecedented level of scams that push the need for better …

Phishpedia: A hybrid deep learning based approach to visually identify phishing webpages

Y Lin, R Liu, DM Divakaran, JY Ng, QZ Chan… - 30th USENIX Security …, 2021 - usenix.org
Recent years have seen the development of phishing detection and identification
approaches to defend against phishing attacks. Phishing detection solutions often report …

Needle in a haystack: Tracking down elite phishing domains in the wild

K Tian, STK Jan, H Hu, D Yao, G Wang - Proceedings of the Internet …, 2018 - dl.acm.org
Today's phishing websites are constantly evolving to deceive users and evade the detection.
In this paper, we perform a measurement study on squatting phishing domains where the …