Abstract
With the rapid development of the power mobile Internet, traditional identity authentication and authentication modes still have identity information theft, are unable to prevent illegal behaviors of internal users, etc., which can no longer meet the security requirements of identity authentication in mobile Internet services. In response to this problem, this paper proposes a mobile terminal identity authentication method on the Identity-Based Cryptography (IBC). During registration, the user’s voice is collected through the mobile terminal to establish an identity vector (i-vector) voiceprint model, and features are extracted and added to the identity mark to generate a user ID and a corresponding identity password system; during authentication, the legitimacy of the user is judged based on voice recognition to prevent illegal user intrusion, And then based on the identity password combined with the symmetric encryption algorithm AES to encrypt and decrypt data to achieve terminal identity authentication to resist common attacks in the mobile Internet. Finally, the experimental analysis shows that the method effectively improves the security of the power mobile Internet identity authentication process and has the advantages of low cost and high efficiency. In the power mobile Internet business scenario, this method greatly improves the identification ability of illegal users and the resistance to network malicious attacks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shamir, A.: Identity-based cryptosystems and signature schemes. In: Blakley, G.R., Chaum, D. (eds.) CRYPTO 1984. LNCS, vol. 196, pp. 47–53. Springer, Heidelberg (1985). https://doi.org/10.1007/3-540-39568-7_5
Boneh, D., Franklin, M.: Identity-based encryption from the weil pairing. In: Kilian, J. (ed.) CRYPTO 2001. LNCS, vol. 2139, pp. 213–229. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44647-8_13
Xu, A., Liu, G., Xu, Y.: Security protection technology of smart substation based on SM9 identification code. Autom. Expo 35(S2), 65–71 (2018)
Ma, X., Ma, W., Liu, X.: Cross-domain authentication scheme based on blockchain technology. Acta Electron. Sin. 46(11), 9 (2018)
Jin, J., Kim, K.: 3D CUBE algorithm for the key generation method: applying deep neural network learning-based. IEEE Access 8, 33689–33702 (2020)
Jhajharia, S., Mishra, S., Bali, S.: Public key cryptography using neural networks and genetic algorithms. In: International Conference on Contemporary Computing. IEEE (2013)
Alimohammadi, N., Shokouhi, B.: Secure hardware key based on Physically Unclonable Functions and artificial Neural Network. In: 2016 8th International Symposium on Telecommunications (IST). IEEE (2016)
Wei, H.: Password Authentication Using Hopfield Neural Networks (2012)
Chakravarthy, A.S.N., Avadhani, P.S., Krishna Prasad, P.E.S.N., et al.: A novel approach for authenticating textual or graphical passwords using hopfield neural network. Adv. Comput.: Int. J. 2(4), 33–42 (2011). https://doi.org/10.5121/acij.2011.2404
Tanaka, A., Kinoshita, K., Kishida, S.: Construction and performance of authentication systems for fingerprint with neural networks. J. Signal Process. 17(1), 1–9 (2013)
Debnath, S., Roy, P.: User authentication system based on speech and cascade hybrid facial feature. Int. J. Image Graph. 20(3), 2050022 (2020)
Bouzalmat, A., Belghini, N., Zarghili, A., et al.: Face recognition using neural network based fourier gabor filters & random projection. Int. J. Comput. Sci. Secur. (IJCSS) 5, 376–386 (2011)
Zhang, Z., Wu, J., Jing, D., et al.:Jamming ACK attack to wireless networks and a mitigation approach. In: Global Telecommunications Conference (2008)
Qiu, M., Gai, K., Xiong, Z.: Privacy-preserving wireless communications using bipartite matching in social big data. Future Gener. Comput. Syst. 87, 772–781 (2018). https://doi.org/10.1016/j.future.2017.08.004
Qiu, H., Qiu, M., Lu, Z.: Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf. Fusion 55, 59–67 (2020)
Lin, S., Xi, S., Lin, et al.: Speaker recognition based on i-vector and deep learning. Comput. Technol. Dev. (2017)
Qiu, F., Hu, K., et al.: Distributed control identity authentication technology of distribution network based on national secret SM9. Comput. App. Softw. 37(09), 291–295 (2020)
Zhu, X., Xu, H.: A fingerprinting authentication system design based on PKI (2006)
Ohkubo, C., Muraoka, Y.: Biometric authentication using PKI. DBLP (2003)
Qiu, H., Dong, T., et al.: Adversarial attacks against network intrusion detection in IoT systems. IEEE Internet of Things J. 8, 10327–10335 (2020)
Qiu, M., Zhang, L., et al.: Security-aware optimization for ubiquitous computing systems with SEAT graph approach. J. Comput. Syst. Sci. 79(5), 518–529 (2013)
Qiu, H., Qiu, M., Liu, M., Memmi, G.: Secure health data sharing for medical cyber-physical systems for the healthcare 4.0. IEEE J. Biomed. Health 24, 2499–2505 (2020)
Wu, G., Zhang, H., Qiu, M., et al.: A decentralized approach for mining event correlations in distributed system monitoring. JPDC 73(3), 330–340 (2013)
Guo, Y., Zhuge, Q., Hu, J., et al.: Optimal data allocation for scratch-pad memory on embedded multi-core systems. In: IEEE ICPP Conference, pp. 464–471 (2011)
Acknowledgment
This work is supported by the State Grid Sichuan Company Science and Technology Project: “Research and Application of Key Technologies of Network Security Protection System Based on Zero Trust Model” (No. SGSCCD00XTJS2101279).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, X., Wang, W., Gan, W., Yang, Y., Yuan, S., Li, M. (2022). Mobile Terminal Identity Authentication Method Based on IBC. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-97774-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97773-3
Online ISBN: 978-3-030-97774-0
eBook Packages: Computer ScienceComputer Science (R0)