[HTML][HTML] A systematic review on deep-learning-based phishing email detection

K Thakur, ML Ali, MA Obaidat, A Kamruzzaman - Electronics, 2023‏ - mdpi.com
Phishing attacks are a growing concern for individuals and organizations alike, with the
potential to cause significant financial and reputational damage. Traditional methods for …

Deep learning for phishing detection: Taxonomy, current challenges and future directions

NQ Do, A Selamat, O Krejcar, E Herrera-Viedma… - Ieee …, 2022‏ - ieeexplore.ieee.org
Phishing has become an increasing concern and captured the attention of end-users as well
as security experts. Existing phishing detection techniques still suffer from the deficiency in …

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] HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system

MA Khan - Processes, 2021‏ - mdpi.com
Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …

Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective

IH Sarker - SN Computer Science, 2021‏ - Springer
Deep learning, which is originated from an artificial neural network (ANN), is one of the
major technologies of today's smart cybersecurity systems or policies to function in an …

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 …

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

IH Sarker - Security and Privacy, 2023‏ - Wiley Online Library
Due to the rising dependency on digital technology, cybersecurity has emerged as a more
prominent field of research and application that typically focuses on securing devices …

A hybrid DNN–LSTM model for detecting phishing URLs

A Ozcan, C Catal, E Donmez, B Senturk - Neural Computing and …, 2023‏ - Springer
Phishing is an attack targeting to imitate the official websites of corporations such as banks,
e-commerce, financial institutions, and governmental institutions. Phishing websites aim to …

[HTML][HTML] A deep learning-based innovative technique for phishing detection in modern security with uniform resource locators

EA Aldakheel, M Zakariah, GA Gashgari, FA Almarshad… - Sensors, 2023‏ - mdpi.com
Organizations and individuals worldwide are becoming increasingly vulnerable to
cyberattacks as phishing continues to grow and the number of phishing websites grows. As …

Deep learning applications in manufacturing operations: a review of trends and ways forward

S Sahoo, S Kumar, MZ Abedin, WM Lim… - Journal of Enterprise …, 2023‏ - emerald.com
Purpose Deep learning (DL) technologies assist manufacturers to manage their business
operations. This research aims to present state-of-the-art insights on the trends and ways …