ITM Web Conf.
Volume 32, 2020International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|Number of page(s)||6|
|Published online||29 July 2020|
Identity Theft Prediction Using Game Theory
1 Department of Computer Engineering, Ramrao Adik Institute of Technology Nerul, Navi Mumbai, India
2 Department of Computer Engineering, Ramrao Adik Institute of Technology Nerul, Navi Mumbai, India
3 Department of Computer Engineering, Ramrao Adik Institute of Technology Nerul, Navi Mumbai, India
4 Department of Computer Engineering, Ramrao Adik Institute of Technology Nerul, Navi Mumbai, India
* e-mail: firstname.lastname@example.org
** e-mail: email@example.com
*** e-mail: firstname.lastname@example.org
**** e-mail: email@example.com
Digital devices have become an integral part of every person’s life. The range of use of these devices is increasing daily. Over the decades, the number of users has increased from thousands to millions and is still increasing. Due to the multi-functional features of digital devices, their importance is now being recognized more than ever. Initially, they were used only for calling and texting; however, nowadays, they are also being used to store relevant data such as account numbers, card numbers, credentials, private pictures, passport copies, etc. The most common form of Identity Theft attack is through stealing passwords. Once the password is stolen, user privacy is lost, and the data is compromised. Thus, a system consisting of a database that comprises of leaked passwords collected from various social sites and common passwords as a part of a dictionary attack used by hackers has been created by us. When a user enters his/her password, it runs it through the database and checks for a match. This document emphasizes on how game theory can be utilized in predicting the possibility of a successful attack and discusses essential concepts such as the various components of game theory and Nash Equilibrium.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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