Applications of deep learning for phishing detection: a systematic literature review

C Catal, G Giray, B Tekinerdogan, S Kumar… - … and Information Systems, 2022 - Springer
Phishing attacks aim to steal confidential information using sophisticated methods,
techniques, and tools such as phishing through content injection, social engineering, online …

A new two-step ensemble learning model for improving stress prediction of automobile drivers

G Issa - The International Arab Journal of …, 2021 - research.skylineuniversity.ac.ae
Commuting when there is a significant volume of traffic congestion has been acknowledged
as one of the key factors causing stress. Significant levels of stress whilst driving are seen to …

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 …

Phishing web site detection using diverse machine learning algorithms

A Zamir, HU Khan, T Iqbal, N Yousaf, F Aslam… - The Electronic …, 2020 - emerald.com
Purpose This paper aims to present a framework to detect phishing websites using stacking
model. Phishing is a type of fraud to access users' credentials. The attackers access users' …

Towards benchmark datasets for machine learning based website phishing detection: An experimental study

A Hannousse, S Yahiouche - Engineering Applications of Artificial …, 2021 - Elsevier
The increasing popularity of the Internet led to a substantial growth of e-commerce.
However, such activities have main security challenges primary caused by cyberfraud and …

Multilayer stacked ensemble learning model to detect phishing websites

LR Kalabarige, RS Rao, A Abraham… - IEEE Access, 2022 - ieeexplore.ieee.org
Phishing is a cyber attack that tricks the online users into revealing sensitive information with
a fake website imitating a legitimate website. The attackers with stolen credentials not only …

It Doesn't Look Like Anything to Me: Using Diffusion Model to Subvert Visual Phishing Detectors

Q Hao, N Diwan, Y Yuan, G Apruzzese… - 33rd USENIX Security …, 2024 - usenix.org
Visual phishing detectors rely on website logos as the invariant identity indicator to detect
phishing websites that mimic a target brand's website. Despite their promising performance …

Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan

W Hadi, N El-Khalili, M AlNashashibi, G Issa… - Computers in biology …, 2019 - Elsevier
Driving daily through traffic congestion has been recognised as a major cause of stress.
High levels of stress while driving negatively impact the driver's decisions which could …

Study of combating technology induced fraud assault (TIFA) and possible solutions: the way forward

M Dadhich, KK Hiran, SS Rao, R Sharma… - … Conference on Emerging …, 2022 - Springer
The study aims to identify modes of fraudulent payments and create awareness of such
incidences to avoid decisive virtual activities. Disruptive developments such as contactless …

Phishing website detection from URLs using classical machine learning ANN model

S Salloum, T Gaber, S Vadera, K Shaalan - International Conference on …, 2021 - Springer
Phishing is a serious form of online fraud made up of spoofed websites that attempt to gain
users' sensitive information by tricking them into believing that they are visiting a legitimate …