Developing adaptive password strength algorithms using deep learning for enhanced cybersecurity
Latika Rahul Desailatikadesai@gmail.comDepartment of Information Technology D. Y. Patil College of Engineering Akurdi Department of Information Technology D. Y. Patil College of Engineering AkurdiPune, Maharashtra, 411044, IndiaView full profile → , *Poorna ShankarCorresponding authorpoornashankar@indiraicem.ac.inDepartment of Computer Application Indira College of Engineering and Management Pune, Maharashtra, 410506, IndiaView full profile → , Jambi Ratna Raja Kumarratnaraj.jambi@gmail.comDepartment of Computer Engineering Genba Sopanrao Moze College of EngineeringPune, Maharashtra, 411045, IndiaView full profile → , Deepika Sharmadeepika.sharma@niu.edu.inDepartment of Computer Sciences Noida International UniversityDepartment of Computer Sciences Noida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile → , L. Mary Gladencemarygladence.it@sathyabama.ac.inDepartment of Information Technology Sathyabama Institute of Science and Technology Chennai, Tamil Nadu, 600119, IndiaView full profile → , Mohammad Alamgir Hossainalamgirhossain.nit@gmail.comDepartment of Computer Science College of Engineering and Computer Science Jazan University Department of Computer Science College of Engineering and Computer Science Jazan University Jazan, Jazan, 82817-2820, Saudi ArabiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 14 Apr 2025
- Published Online:
- 31 Dec 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2502
- Pages:
- 599–607
Abstract
Keywords
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References
[1] K. H. Hong and B. M. Lee, “A deep learning-based password security evaluation model,” Appl. Sci., vol. 12, no. 5, pp. 2404 (2022), doi: 10.3390/app12052404.
[2] E. Darbutaitė, P. Stefanovič, and S. Ramanauskaitė, “Machine-learning-based password-strength-estimation approach for passwords of Lithuanian context,” Appl. Sci., vol. 13, no. 13, pp. 7811 (2023), doi: 10.3390/app131378.
[3] A. P. Umejiaku, P. Dhakal, and V. S. Sheng, “Balancing password security and user convenience: Exploring the potential of prompt models for password generation,” Electronics, vol. 12, no. 10, pp. 2159 (2023), doi: 10.3390/electronics12102159.
[4] A. K. Al Hwaitat and H. N. Fakhouri, “Adaptive cybersecurity neural networks: An evolutionary approach for enhanced attack detection and classification,” Appl. Sci., vol. 14, no. 19, pp. 9142 (2024), doi: 10.3390/app14199142.
[5] M. Rusanov, M. Babenko, M. Lapina, and M. Sajid, “Identification of exploited unreliable account passwords in the information infrastructure using machine learning methods,” Big Data Cogn. Comput., vol. 8, no. 11, pp. 159 (2024), doi: 10.3390/bdcc8110159.
[6] Y. Mo, S. Li, Y. Dong, Z. Zhu, and Z. Li, “Password complexity prediction based on RoBERTa algorithm,” Appl. Sci. Eng. J. Adv. Res., vol. 3, pp. 1–5 (2024).
[7] E. Darbutaitė, P. Stefanovič, and S. Ramanauskaitė, “Machine-learning-based password-strength-estimation approach for passwords of Lithuanian context,” Data-Driven Cybersecurity and Privacy Analytics, vol. 13, pp. 7811 (2023).
[8] V. Belikov and I. Prokuronov, “Password strength verification based on machine learning algorithms and LSTM recurrent neural networks,” Russ. Technol. J., vol. 11, pp. 7–15 (2023).
[9] Priti Bansal, Rishabh Lamba, Vaibhav Jain, Tanmay Jain, Sanchit Shokeen, Sumit Kumar, Pradeep Kumar Singh, and Baseem Khan, “GGA-MLP: A greedy genetic algorithm to optimize weights and biases in multilayer perceptron,” Contrast Media Mol. Imaging, vol. 2022, pp. 4036035 (2022).
[10] D. Dhabliya, S. Kunche, S. Dingankar, S. S. Dari, R. Dhabliya, and V. Khetani, “Blockchain technology as a paradigm for enhancing cyber security in distributed systems,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 2-B, pp. 729–740 (2024).
