Using machine learning for phishing domain detection [Tutorial] | Tutorials - Packt Hub

Photo: Chiheb Chebbi

This article is an excerpt from a book written by Chiheb Chebbi, InfoSec enthusiast titled Mastering Machine Learning for Penetration Testing. In this book, you will you learn how to identify loopholes in a self-learning security system and will be able to efficiently breach a machine learning system.

Mastering Machine Learning 
 for Penetration Testing

Social engineering is one of the most dangerous threats facing every individual and modern organization. Phishing is a well-known, computer-based, social engineering technique. Attackers use disguised email addresses as a weapon to target large companies. With the huge number of phishing emails received every day, companies are not able to detect all of them. That is why new techniques and safeguards are needed to defend against phishing. This article will present the steps required to build three different machine learning-based projects to detect phishing attempts, using cutting-edge Python machine learning libraries.

The Social Engineering Engagement Framework (SEEF) is a framework developed by Dominique C. Brack and Alexander Bahmram. It summarizes years of experience in information security and defending against social engineering. The stakeholders of the framework are organizations, governments, and individuals (personals).
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Source: Packt Hub