Journal Article

Robust password security: a genetic programming approach with imbalanced dataset handling

Baressi S̆egota, Sandi, Car, Zlatan, Andelić, Nikola

2024

Overview

Developing a method for determining password strength using artificial intelligence (AI) is crucial as it enhances cybersecurity by providing a more robust defense against unauthorized access. AI can analyze complex patterns and trends, allowing for the identification of weak passwords and potential vulnerabilities more effectively than traditional methods. This proactive approach helps users and organizations strengthen their security posture, reducing the risk of data breaches and unauthorized intrusions. In this paper, the genetic programming symbolic classifier (GPSC) was applied to the publicly available dataset to obtain a set of symbolic expressions for password strength classification with high classification accuracy. One of the problems with the dataset was an imbalance between classes so various oversampling/undersampling techniques have been utilized. The optimal GPSC hyperparameter values were found using the random hyperparameter value search method. The algorithm was trained using fivefold cross-validation (5FCV). One of the problems with the dataset was an imbalance between classes so various oversampling/undersampling techniques have been utilized. To evaluate obtained SEs, the evaluation metric accuracy, area under receiver operating characteristics curve, precision, recall, and f 1-score were used. The obtained SEs on balanced dataset variations achieved high classification accuracy (0.99) and with the application of all SEs on the entire original imbalanced dataset achieved the same accuracy.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Access control / Accuracy / Artificial intelligence / Classification / Classifiers / Coding and Information Theory / Communications Engineering / Computer Communication Networks / Computer Science / Cryptology / Cybersecurity / Data integrity / Datasets / Decision trees / Deep learning / Genetic algorithms / Genetics / Imbalance / Management of Computing and Information Systems / Mathematical functions / Networks / Neural networks / Operating Systems / Oversampling / Passwords / Regular Contribution / Research methodology / Robustness / Security / Support vector machines / Symbolism / Unauthorized