phishing detection

The phishing detection is the area I've chosen to look at more deeply. Phishing is a type of fraud in which con artists attempt to get user information, such as usernames, passwords, credit card or bank account information, by posing as a reliable business online. Such information might be used maliciously for a variety of purposes, frequently to steal money from victims. The safety of the data and the money is maintained through prompt detection of phishing activities.

The problem of phishing causes multi-billion dollar losses for regular people and companies; elderly people are particularly susceptible to this sort of fraud, hence this area is crucial for the upcoming study. On the other hand, this area provides numerous opportunities for machine learning application. Therefore, the problem has a high social and technical significance.

The article discussing the problem:

Abdelhamid, N., Thabtah, F., & Abdel-jaber, H. (2017, July). Phishing detection: A recent intelligent machine learning comparison based on models content and features. Intelligence and Security Informatics (ISI), a 2017International Conference on IEEE, 72-77.

The video discussing the problem:

Ironscales - World’s 1st automated phishing prevention, detection & response. YouTube. Retrieved from https://www.youtube.com/watch?v=giaxfViqDeg.

After reading and viewing resources, please answer the following questions:

1. What are classic anti-phishing approaches?

2. What is the method of detection applied in Ironscales program solution?

3. What are key criteria used for ML models comparison in the article?