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Enhancing graphical password authentication system with deep learning-based arabic digit recognition

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Abstract

The purpose of this research paper is to introduce a new graphical password system that combines the strengths of traditional passwords, such as flexibility, with the benefits of graphical passwords, such as ease of use, memory retention, and security. The new system is based on drawing rather than selecting images, and it uses deep learning models to classify the drawn images. To improve network performance in terms of storage and data required to send, a new method called “selected pixels (SP)” has been proposed. This method sends only the color pixels of the drawing images rather than the whole image. The proposed system is a highly flexible platform that can be easily integrated into any e-commerce ecosystem. It is specifically designed to accept Arabic digits, but it is not limited to this type of input. The system can be adapted to accept other types of digits, characters, or even objects. The proposed system has been evaluated using login time, the total amount of data required to send and store, and password entropy. The results of the evaluation show that the new system outperforms traditional graphical password systems in terms of all the evaluated metrics. Therefore, the proposed system has significant potential to be used as a new and effective graphical password system.

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References

  1. Abdleazeem S, El-Sherif Ezzat (2008) Arabic handwritten digit recognition. Int J Doc Anal Recogn IJDAR 11:127–141

    Article  Google Scholar 

  2. Aloysius N, Geetha M (2017) A review on deep convolutional neural networks. In: 2017 international conference on communication and signal processing (ICCSP). IEEE, pp. 0588–0592

  3. Andriotis P, Kirby M, Takasu A (2022) Bu-dash: a universal and dynamic graphical password scheme. In: HCI for Cybersecurity, Privacy and Trust: 4th International Conference, HCI-CPT 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26–July 1, 2022, Proceedings. Springer, pp 209–227

  4. Bhanushali A, Mange B, Vyas H, Bhanushali H, Bhogle P (2015) Comparison of graphical password authentication techniques. Int J Comput Appl 116(1):96

    Google Scholar 

  5. Gokhale MAS, Waghmare VS (2016) The shoulder surfing resistant graphical password authentication technique. Procedia Comput Sci 79:490–498

    Article  Google Scholar 

  6. Islam A, Por LY, Othman F, Ku CS (2019) A review on recognition-based graphical password techniques. In: Computational Science and Technology: 5th ICCST 2018, Kota Kinabalu, Malaysia, 29-30 August 2018, pp 503–512

  7. Jirjees SW, Mahmood AM, Nasser AR (2022) Passnumbers: an approach of graphical password authentication based on grid selection. IJSSE 12(1):21–29

    Article  Google Scholar 

  8. Kayem Anne VDM (2016) Graphical passwords—a discussion. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, pp 596–600

  9. Khodadadi T, Javadianasl Y, Rabiei F, Alizadeh M, Zamani M, Chaeikar SS (2021) A novel graphical password authentication scheme with improved usability. In: 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). IEEE, pp 01–04

  10. Kulkarni TR, Dushyanth ND (2021) Performance evaluation of deep learning models in detection of different types of arrhythmia using photo plethysmography signals. Int J Inf Technol 13:2209–2214

    Google Scholar 

  11. Matta P, Pant B (2020) TCPC: a graphical password scheme ensuring authentication for IOT resources. Int J Inf Technol 12:699–709

    Google Scholar 

  12. Meng W, Zhu L, Li W, Han J, Li Y (2019) Enhancing the security of fintech applications with map-based graphical password authentication. Future Gener Comput Syst 101:1018–1027

    Article  Google Scholar 

  13. Orunsolu AA et al (2022) An efficient and secured graphical authentication system. Acta Inform Malays (AIM) 6(1):17–21

    Article  Google Scholar 

  14. Pang G, Shen C, Cao L, Van Den Hengel A (2021) Deep learning for anomaly detection: a review. ACM Comput Surv (CSUR) 54(2):1–38

    Article  Google Scholar 

  15. Rani Geeta, Pandey Upasana, Wagde Aniket Anil, Dhaka Vijaypal Singh (2023) A deep reinforcement learning technique for bug detection in video games. Int J Inf Technol 15(1):355–367

    Google Scholar 

  16. Singh UP, Chouhan SS, Jain S (2020) Images as graphical password: verification and analysis using non-regular low-density parity check coding. Int J Inform Technol. https://doi.org/10.1007/s41870-020-00477-x

    Article  Google Scholar 

  17. Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E et al (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci. https://doi.org/10.1155/2018/7068349

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wang Z, Liao L, Meng R, Yang C-N, Zhou Z, Yang H (2022) Verification grid and map slipping based graphical password against shoulder-surfing attacks. Secur Commun Netw. https://doi.org/10.1155/2022/6778755

    Article  Google Scholar 

  19. Warnecke A, Arp D, Wressnegger C, Rieck K (2020) Evaluating explanation methods for deep learning in security. In: 2020 IEEE European symposium on security and privacy (EuroS &P). IEEE, pp 158–174

  20. Yang G-C (2017) Passpositions: a secure and user-friendly graphical password scheme. In: 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT). IEEE, 1–5

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Authors and Affiliations

  1. Al-Nahrain University, Baghdad, Iraq

    Areeg Fahad Rasheed

  2. Baghdad, Iraq

    M. Zarkoosh

  3. Wellington, New Zealand

    F. R. Elia

Authors
  1. Areeg Fahad Rasheed
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  2. M. Zarkoosh
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  3. F. R. Elia
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Corresponding author

Correspondence to Areeg Fahad Rasheed.

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Conflict of interest

“We declare that we have no conflicts of interest related to this research. The study was conducted without any external influence from personal, financial, or professional relationships that could potentially bias the data or the interpretation of the results. We have no financial, personal or other relationships with individuals or organizations that could inappropriately influence our work.”

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Rasheed, A.F., Zarkoosh, M. & Elia, F.R. Enhancing graphical password authentication system with deep learning-based arabic digit recognition. Int. j. inf. tecnol. 16, 1419–1427 (2024). https://doi.org/10.1007/s41870-023-01561-8

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  • DOI: https://doi.org/10.1007/s41870-023-01561-8

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