[HTML][HTML] A deep learning-based phishing detection system using CNN, LSTM, and LSTM-CNN

Z Alshingiti, R Alaqel, J Al-Muhtadi, QEU Haq… - Electronics, 2023 - mdpi.com
In terms of the Internet and communication, security is the fundamental challenging aspect.
There are numerous ways to harm the security of internet users; the most common is …

[HTML][HTML] 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 …

[HTML][HTML] Intelligent decision forest models for customer churn prediction

FE Usman-Hamza, AO Balogun, LF Capretz… - Applied Sciences, 2022 - mdpi.com
Customer churn is a critical issue impacting enterprises and organizations, particularly in the
emerging and highly competitive telecommunications industry. It is important to researchers …

[HTML][HTML] Phishing email detection model using deep learning

S Atawneh, H Aljehani - Electronics, 2023 - mdpi.com
Email phishing is a widespread cyber threat that can result in the theft of sensitive
information and financial loss. It uses malicious emails to trick recipients into providing …

Ensemble-based logistic model trees for website phishing detection

VE Adeyemo, AO Balogun, HA Mojeed… - Advances in Cyber …, 2021 - Springer
The adverse effects of website phishing attacks are often damaging and dangerous as the
information gathered from unsuspecting users are used inappropriately and recklessly …

[HTML][HTML] Empirical analysis of rank aggregation-based multi-filter feature selection methods in software defect prediction

AO Balogun, S Basri, S Mahamad, SJ Abdulkadir… - Electronics, 2021 - mdpi.com
Selecting the most suitable filter method that will produce a subset of features with the best
performance remains an open problem that is known as filter rank selection problem. A …

[PDF][PDF] PHIBOOST-a novel phishing detection model using Adaptive boosting approach

A Odeh, I Keshta, E Abdelfattah - Jordanian Journal of Computers …, 2021 - researchgate.net
Every day, cyberattacks increase and use different strategies. One of the most common
cyberattacks is Phishing, where the attacker collects sensitive and confidential information …

[HTML][HTML] An adaptive rank aggregation-based ensemble multi-filter feature selection method in software defect prediction

AO Balogun, S Basri, LF Capretz, S Mahamad… - Entropy, 2021 - mdpi.com
Feature selection is known to be an applicable solution to address the problem of high
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …

[HTML][HTML] Empirical analysis of forest penalizing attribute and its enhanced variations for android malware detection

AG Akintola, AO Balogun, LF Capretz, HA Mojeed… - Applied Sciences, 2022 - mdpi.com
As a result of the rapid advancement of mobile and internet technology, a plethora of new
mobile security risks has recently emerged. Many techniques have been developed to …

Phish-Sight: a new approach for phishing detection using dominant colors on web pages and machine learning

P Pandey, N Mishra - International Journal of Information Security, 2023 - Springer
Phishing is one of the most dangerous threats in which a hacker imitates a person, company
or government agency to lure and deceive their victims. Machine learning anti-phishing …