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Title |
Bot Impact Efficiency Assessment in Password Evaluation |
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Author |
Rawabi Alharthi and Samah Alajmani |
| Citation |
Vol. 25 No. 4 pp. 249-258 |
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Abstract |
It is no secret to all of us how important passwords are in our lives. As we know, they are the first line of defense to protect our privacy, the security of our accounts, and even our money and property. With cyber-attacks on the rise and attacks becoming more frequent in general, including attacks targeting passwords, it is imperative to raise awareness of choosing strong passwords so that users can ensure the protection of their sensitive information and accounts. Therefore, on the other hand, it is necessary to try to use all techniques and methods to confront these different attacks. Many methods have been developed to evaluate the passwords chosen by users to determine their strength or weakness and to experiment with different algorithms. Therefore, this paper aims to evaluate and measure the effectiveness of using a bot program to evaluate passwords using a dataset, where three bots were created independently of each other and each bot was evaluated separately. The results indicate that the self-learning bot achieved the highest level among the other bots, which helps the user to discover the strength of his passwords early before falling victim to various password attacks. |
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Keywords |
Password Attacks, Passwords Vulnerability, Bot, Security, LightGBM, Machine Learning. |
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