Predicting phishing websites based on self-structuring neural network

By Rami Mustafa A. Mohammad, Fadi A. Thabtah, L. Mccluskey. Neural computing & applications (Print), 2013

351 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2ba9e8879cc191f9bf68e733cd212b4f7edc1670

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Fadi A. Thabtah

In Information Systems

1. Fadi A. Thabtah
2. Chaminda T. E. R. Hewage
3. Imtiaz A. Khan
4. Zainab Alkhalil
5. Liqaa F. Nawaf
6. Rory Bunker
7. Cynthia Rudin

In Signal Processing

1. Fadi A. Thabtah
2. Chaminda T. E. R. Hewage
3. Imtiaz A. Khan
4. Zainab Alkhalil
5. Liqaa F. Nawaf

Citations in the corpus

• Phishing Attacks: A Recent Comprehensive Study and a New Anatomy (2021, background): “A year later, the ‘‘ Anti-Phishing Act ’’ was introduced in the US Congress in March 2005 (Mohammad et al., 2014).”
• A machine learning framework for sport result prediction (2019, background): “This may lead in some cases to the problem of overfitting, as well as wasting computing resources such as training time and memory [27].”