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Case Study: Confusion Matrix in Cyber Security(Phising Attack)
What is Phising Attack?
Phishing is a type of social engineering attack often used to steal user data, including login credentials and credit card numbers. It occurs when an attacker, masquerading as a trusted entity, dupes a victim into opening an email, instant message, or text message. The recipient is then tricked into clicking a malicious link, which can lead to the installation of malware, the freezing of the system as part of a ransomware attack or the revealing of sensitive information.
Phishing Detection Using Machine Learning Techniques
Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. Phishers try to deceive their victims by social engineering or creating mock-up websites to steal information such as account ID, username, password from individuals and organizations. One of the most successful methods for detecting these malicious activities is Machine Learning. This is because most Phishing attacks have some common characteristics which can be identified by machine learning methods.
So after building a machine learning model and trained it on some data of Phising attacks … now what? How to evaluate the accuracy of the model?
Since this model is a classifier model, one of the important metrics than can be used for evaluating the accuracy and also other things is Confusion Matrix.
A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known
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