Reconnaissance of Credentials through Phishing Attacks & it’s Detection using Machine Learning
Mohd. Altamash, Shailendra Narayan Singh · 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) · 2022
A cybercrime is what that best describes a Phishing Attack through which even a normal citizen of a country copies a true person/institution by encouraging them as an official person via e-mail or other means of communication. A person who is prone to do malicious things is known as an attacker who sends malicious Links or Payloads which may evolve into cyber attack via phishing e-mails that can execute multiple tasks, including capturing the victim’s login credentials or account data.. Due to cash loss and identity theft, these e-mails damage victims in numerous ways as in economically, mentally and much more. In this study, we have done phishing attacks which is a malicious act, usually made through email, to steal personal and private data of the Users without their knowledge. We have performed this using LinkedIn and Facebook Login Pages so that the attack could appear to the victim as a real scenario. Somehow, the attacker manipulates the users such a way that the user visits a faked web site which is send by the attackers through faked e-mails or instant messages, and without a sound gives his personal information such as user name, password, i.e. Confidential data and other private data which is of utmost importance to the victim to the user unknowingly. The method to perform this Attack is described in this study. Several techniques are mentioned for the purpose of accomplishing phishing attack and eventually providing prevention method to avoid phishing attack. We have also covered the ways in which Machine Learning Algorithms could somehow detect the Phishing Links & could play a essential method in detecting and preventing from serious loss of data i.e. Loss of Credentials. We will also learn ways in which we can classify b/w Malicious Links and other normal Links.