Abstract
The core idea of ensuring network system security is identity recognition of the user. However, how to identify hackers after breaking through the existing system access control mechanism is still an important problem to be resolved. Therefore, this paper proposes an identity recognition method based on hierarchical behavior characteristics of network users. Behavior of network user was divided into interactive behavior characteristic and mouse behavior characteristic. After characteristics fusion, the Random Forest (RF) method was used to construct the user’s identification model. And the identification results of single level behavior characteristics were compared with the results of this paper. The results show that the average True Acceptance Rate (TAR) and False Acceptance Rate (FAR) of 8 users’ identity recognition were 82.73% and 7.26%, respectively, which is better than the identification result of single level behavior characteristics. This study provides a new idea for identity recognition based on user behavior. Combining the user’s macro interaction behavior characteristics and micro mouse behavior characteristics in a short time or with a small amount of data can better identify users. This adds a layer of security protection for network security.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
New Toyota Data Breach Exposes Personal Information of 3.1 Million Customers [EB/OL]. https://www.cpomagazine.com/cyber-security/new-toyota-data-breach-exposes-personal-information-of-3-1-million-customers/. Accessed 09 Apr 2019
Biddle, R., Mannan, M., Van Oorschot, P.C., et al.: User study, analysis, and usable security of passwords based on digital objects. IEEE Trans. Inf. Forensics Secur. 6(3), 970–979 (2011)
Wang, Y., Hu, J.: Global ridge orientation modeling for partial fingerprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 72–87 (2011)
Jing, X., Li, S., Zhang, D., Lan, C., Yang, J.: Optimal subset-division based discrimination and its kernelization for face and palmprint recognition. Pattern Recogn. 45(10), 3590–3602 (2012)
Mengyao, X., Yan, F., Wang, B., Yi, S., Yi, Q., Xiong, S.: Construction of network user behavior spectrum in big data environment. In: Li, K., Fei, M., Dajun, D., Yang, Z., Yang, D. (eds.) Intelligent Computing and Internet of Things, pp. 133–143. SpringerS, Singapore (2018). https://doi.org/10.1007/978-981-13-2384-3_13
Buriro, A., Crispo, B., Conti, M.: Answer authentication: a bimodal behavioral biometric-based user authentication scheme for smartphones. J. Inf. Secur. Appl. 44, 89–103 (2019)
Shen, C., Cai, Z., Guan, X., et al.: User authentication through mouse dynamics. IEEE Trans. Inf. Forensics Secur. 8(1), 16–30 (2012)
Bailey, K.O., Okolica, J.S., Peterson, G.L.: User identification and authentication using multi-modal behavioral biometrics. Comput. Secur. 43, 77–89 (2014)
Yi, Q., Xiong, S., Wang, B., et al.: Identification of trusted interactive behavior based on mouse behavior considering web user’s emotions. Int. J. Ind. Ergon. 76, 102903 (2020)
Wieman, H.N.: The unique in human behavior. Psychol. Rev. 29(6), 414 (1922)
Kang, P., Cho, S.: Keystroke dynamics-based user authentication using long and free text strings from various input devices. Inf. Sci. 308, 72–93 (2015)
Salem, A., Obaidat, M.S.: A novel security scheme for behavioral authentication systems based on keystroke dynamics. Secur. Priv. 2(2), e64 (2019)
Damopoulos, D., Kambourakis, G., Gritzalis, S.: From key loggers to touch loggers: take the rough with the smooth. Comput. Secur. 32, 102–114 (2013)
Everitt, R.A.J., McOwan, P.W.: Java-based internet biometric authentication system. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1166–1172 (2003)
Zheng, N., Paloski, A., Wang, H.: An efficient user verification system via mouse movements. In: ACM Conference on Computer & Communications Security. ACM (2011)
Hu, T., Niu, W., Zhang, X., et al.: An insider threat detection approach based on mouse dynamics and deep learning. Secur. Commun. Netw. 2019, 1–12 (2019)
Kołakowska, A.: Usefulness of keystroke dynamics features in user authentication and emotion recognition. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human-Computer Systems Interaction. AISC, vol. 551, pp. 42–52. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-62120-3_4
Sayed, B., Traoré, I., Woungang, I., et al.: Biometric authentication using mouse gesture dynamics. IEEE Syst. J. 7(2), 262–274 (2013)
Alpar, O.: Frequency spectrograms for biometric keystroke authentication using neural network based classifier. Knowl. Based Syst. 116, 163–171 (2017)
Shen, C., Cai, Z., Liu, X., et al.: Mouse identity: modeling mouse-interaction behavior for a user verification system. IEEE Trans. Hum. Mach. Syst. 46(5), 734–748 (2016)
Chong, P., Elovici, Y., Binder, A.: User authentication based on mouse dynamics using deep neural networks: a comprehensive study. IEEE Trans. Inf. Forensics Secur. 15, 1086–1101 (2019)
Nguyen, T.T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tut. 10(4), 56–76 (2009)
Hämäläinen, W., Vinni, M.: Classifiers for educational data mining. In: Handbook of Educational Data Mining. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, pp. 57–71 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, B., Zhai, Z., Gao, B., Zhang, L. (2021). Identity Recognition Based on the Hierarchical Behavior Characteristics of Network Users. In: Moallem, A. (eds) HCI for Cybersecurity, Privacy and Trust. HCII 2021. Lecture Notes in Computer Science(), vol 12788. Springer, Cham. https://doi.org/10.1007/978-3-030-77392-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-77392-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77391-5
Online ISBN: 978-3-030-77392-2
eBook Packages: Computer ScienceComputer Science (R0)