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001-es BibID:	BIBFORM116535
035-os BibID:	(WoS)001104140800005 (Scopus)85176785203
Első szerző:	Kovács Ádám
Cím:	CAPTCHA recognition using machine learning algorithms with various techniques / Kovács, Ádám; Tajti, Tibor
Dátum:	2023
ISSN:	1787-5021 1787-6117
Megjegyzések:	In this paper, we present research results on the recognition of text-based CAPTCHA tests using advanced machine learning algorithms and techniques. Text-based CAPTCHAs serve as a crucial security measure to prevent automated access to various web services, but their effectiveness de-pends on their resistance to sophisticated recognition techniques. To this end, we focus on evaluating and enhancing the performance of recognition models using a Convolutional Neural Network (CNN) as the base model. We propose an integrated approach, which incorporates a systematic parameter optimization strategy using Grid Search Cross-Validation (Grid Search CV) and the Ensemble Voting Method to improve the performance of the recognition model. The use of Grid Search CV enables us to fine-tune the hyperparameters of the CNN model, leading to an optimal configuration. Further, we investigate the effectiveness of the Ensemble Voting Method to aggregate the predictions from multiple CNN models, each with a set of the optimal parameters obtained from the Grid Search CV. The methods' performance was evaluated through multiple learning sessions, assessing their effectiveness in recognizing text-based CAPTCHAs under various scenarios. ? 2023, Eszterhazy Karoly College. All rights reserved.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény hazai lapban folyóiratcikk CAPTCHA recognition ensemble methods hyperparameter optimization Machine learning neural networks
Megjelenés:	Annales Mathematicae et Informaticae. - 58 (2023), p. 81-91. -
További szerzők:	Tajti Tibor Gábor (1970-) (informatikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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