Improving Net Banking Security with Face Recognition Based Bio-Metric Verification

Authors

  • V. Manju  Assistant Professor, M.E. Department of Computer Science and Engineering, Sri Raaja Raajan College of Engineering and Technology, karaikudi, Tamil Nadu, India
  • S. Madhumathi  M.E., Department of Computer Science and Engineering, Sri Raaja Raajan College of Engineering and Technology, Karaikudi, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT195335

Keywords:

Secure Net Banking, Multi party access, ICP Feature detection, KNN Classification

Abstract

Internet banking services must be more responsive towards security requirements. Now a days with the network world, the way for cybercrime is become easier for hacking purpose. Because of this reason, network security has become one of the biggest concerns of today security environment. While there is no doubt that Internet banking transaction must have layered safety towards protection threats, the vendors should technique protection issues as part of their provider services. And heard a lot about hackers and crackers ways to steal any logical password or pincode number character, crimes of ID cards or credit cards fraud or security breaches. In existing work, Identification can be processed to a username and is used to authorize access to a system. As usernames can be lost or stolen, it is necessary to validate that the intended user is really the person he or she claims to be – the authentication process. Biometric based totally authentication and identification structures are the new answers to deal with the issues of safety and privacy. The Face Recognition is the examine of physical or behavioral traits of individual used for the identification of individual. These biometric characteristics of a person include the various features like fingerprints, face, hand geometry, voice, and iris biometric device. Here implement real time secure authentication system using face biometrics for authorized the person for online banking system. The general objective of our project is to develop fully functional face recognition, verification system provide and understand the key aspects of these major technologies, social environmental system and performance aspects. And also provide multiparty access system to allow the multiple persons to access the same accounts by providing access privileges to original account holders. Experimental results show that the proposed system provide high level security in online transaction system than the existing traditional cryptography approach

References

  1. Visual Analytics for Fraud Detection and Monitoring Author: Roger A. Leite, Theresia Gschwandtner, Silvia Miksch, Erich Gstrein, and Johannes Kuntner Year: 2013
  2. Visual Analytics for Fraud Detection: Focusing on Profile Analysis Author: Roger Almeida Leite, Theresia Gschwandtner, Silvia Miksch, Erich Gstrein & Johannes Kuntner Year: 2016
  3. Data visualization for fraud detection: Practice implications and a call for future research Author: William N. Dilla a, Robyn L. Raschke b Year: 2015
  4. Cross-Domain Deep Face Matching for Real Banking Security Systems Authors:Johnatan S. Oliveira, Gustavo B. Souza, Anderson R. Rocha, Flavio E. Deus and Aparecido N. Marana Year: 2018
  5. Securing ATM by Image Processing – Facial Recognition Authentication, Authors: T. Suganya, T. Nithya C. Sunitha, B. Meena Preethi, Year: 2015
  6. Secured Banking operations with face-based Automated Teller Machine, Authors: Olutola Fagbolu1, Olumide Adewale2 Boniface Alese2and Osuolale Festus2, Year: 2014
  7. Face Detection based Locker Security System using Raspberry Pi Authors:Sandeep V, Guruprasad Hegde , Chetan N, Girish P Patil, Lad Bhavesh Year: 2016
  8. L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Computer networks, vol. 54, no. 15, pp. 2787-2805, 2010.
  9. S. Chen, H. Xu, D. Liu, and B. Hu, “A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective,” IEEE Internet of Things Journal, vol. 1, no. 4, pp. 349-359, 2014.
  10. H. Ning, H. Liu, J. Ma, L. T. Yang, and R. Huang, “Cybermatics: Cyberphysical- social-thinking hyperspace based science and technology,” Future Generation Computer Systems, vol. 56, pp. 504-522, 2016.
  11. T. Qiu, N. Chen, K. Li, D. Qiao, and Z. Fu, “Heterogeneous ad hoc networks: Architectures, advances and challenges,” Ad Hoc Networks, vol. 55, pp. 143-152, 2017.
  12. Yu, Lei, et al. "CoRE: Cooperative end-to-end traffic redundancy elimination for reducing cloud bandwidth cost." IEEE Transactions on Parallel and Distributed Systems 28.2 (2017): 446-461.
  13. J. Yuan and S. Yu, “Efficient privacy-preserving biometric identificationin cloud computing,” in 2013 Proceedings IEEE INFOCOM, 2013, pp. 2652-2660.
  14. Z. Xia, X. Wang, L. Zhang, Z. Qin, X. Sun, and K. Ren, “A Privacy- Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 11, pp. 2594-2608, 2016.
  15. K. Lee, D. Kim, D. Ha, U. Rajput, and H. Oh, “On security and privacy issues of fog computing supported Internet of Things environment,” in 2015 6th International Conference on the Network of the Future (NOF), 2015, pp. 1-3.

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Published

2019-06-30

Issue

Section

Research Articles

How to Cite

[1]
V. Manju, S. Madhumathi, " Improving Net Banking Security with Face Recognition Based Bio-Metric Verification, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 3, pp.82-91, May-June-2019. Available at doi : https://doi.org/10.32628/CSEIT195335