Phishing detection based Associative Classification data mining

By Neda Abdelhamid, Aladdin Ayesh, Fadi A. Thabtah. Expert systems with applications, 2014

405 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/867e2293e9780b729705b4ba48d6b11e3778e999

How researchers see this paper

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

In Artificial Intelligence

1. Doyen Sahoo
2. Zibin Zheng
3. Vadlamani Ravi
4. B. Shravan Kumar
5. Steven C. H. Hoi
6. Chenghao Liu
7. Chuan Chen
8. Longbing Cao

In Computer Networks and Communications

1. Zibin Zheng
2. Steven C. H. Hoi
3. Chenghao Liu
4. Doyen Sahoo
5. Michael R. Lyu

In Information Systems

1. Zibin Zheng
2. Vadlamani Ravi
3. B. Shravan Kumar
4. Steven C. H. Hoi
5. Chenghao Liu
6. Doyen Sahoo
7. Michael R. Lyu

Citations in the corpus

• Malicious URL Detection using Machine Learning: A Survey (2017, background): “For dimensionality size, the lexical features have a very high-dimensionality (and so do unstructured Host-features and content features).”
• Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding (2019, background): “of impersonating a website of an honest firm aiming to acquire users’ private information, such as usernames, passwords, and social security numbers [21].”
• A survey of the applications of text mining in financial domain (2016, background): “Abdelhamid et al. (2014) presented a review paper on “Phishing Detection based on Association Rule Mining Technique”.”