ENHANCED PASSWORD RECOVERY THROUGH USER PROFILING

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ENHANCED PASSWORD RECOVERY THROUGH USER PROFILING

Abstract
Red-teaming and penetration testing require the capacity to recover passwords, which can assist users in preventing data loss if a password is lost and the data is password-protected. In the present thesis, user profiling is used to investigate password recovery. It is possible to investigate if gender and region will improve password recovery and whether there are gender- or location-related biases by using these data points to profile users. A given username will be used to train machine learning models to estimate a user’s gender, and an email address’s top-level domain will be used to classify a user’s region. Improved Wasserstein Generative Adversarial Networks are the foundation of a generative model that is taught to capture dissemination of passwords and thereafter create its test samples.

The findings will demonstrate that gender and area data points will improve password recovery on their own and that when combined, they will produce the best outcomes. However, the results of various combinations of the data points will vary, and this is further discussed in the paper.
Future developments on this subject are now possible because of this. More information can be added to the thesis’ discriminating section to further improve the accuracy of password recovery. The purpose is to educate people about password preferences so they are more aware of the passwords’ vulnerabilities.

Table of Contents

Approval….………………………………………………………………………………………ii

Declaration iii
Acknowledgment iv
Table of contents v
List of Figure vi
List of Table vii
List of Nomenclature viii
Abstract ix

Chapter 1: Introduction ……………………………………………………………………….
1.1 Problem Description ……………………………………………………………………….
1.2 Motivation of the research …………………………………………………………………
1.3 Objective of the research……………………………………………………………………
1.4 Research Design …………………………………………………………………….……..

Chapter 2: Background Study………………………………………………………..……….

Chapter 3: Research Methodology……….…………………………………………….….…
3.1 Dataset Explanation………………………………………………………………………
3.2 Data Pre-processing ……………………………………………………………..….……
3.3 Performance Measure Parameter ………………………………………………..……….
3.4 Ensemble classification algorithm …………………………………………….………..

Chapter 4: Result & Discussion………………………………………………………………..

Chapter 5: Conclusion & Recommendation…………………………………………………

Reference………………………………………………………………………………………

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