Abstract
With the rapid development of modern mobile Internet services, the business architecture and network environment of power mobile Internet are also undergoing significant changes. In view of the current network security protection method of tradition is very difficult to adapt to the safety of power for mobile business diversification demand, unable to effectively defense complex network attacks and threats, internal network security accidents frequent this present situation, proposed a based on the difference of privacy and UEBA (User Entity behaviors Analytics) of the power of mobile Internet network security situational awareness model. UEBA is used to realize network situation awareness of power mobile interconnection business terminals, and the privacy of user data is effectively protected by introducing differential privacy mechanisms. At the same time, aiming at the shortcoming of a high false-positive rate of first access warning in UEBA, the optimization of the first access evaluation mechanism is introduced, and the recommendation score between users and visiting entities is predicted by the method based on the recommendation system. Experimental analysis shows that the proposed method can effectively reduce the false alarm rate of first access warnings. And compare our method with the general situation awareness scheme, it has obvious advantages.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sood, I., Sharma, V.: Computational intelligent techniques to detect ddos attacks: a survey. J. Cyber Secur. 3(2), 89–106 (2021)
Si, D., Hua, C., Yang, H.: A security threat analysis system based on machine learning. Inf. Technol. Netw. Secur. 4 (2019)
Bass, T.: Multisensor data fusion for next generation distributed intrusion detection systems. In: Proceedings of the IRIS National Symposium on Sensor and Data Fusion, vol. 24, no. 28, pp. 24–27. COAST Laboratory, Purdue University, l (1999)
Bass, T.: Intrusion systems and multisensory data fusion. Commun. ACM 43(4), 99–105 (2000)
Xu, F.: Status and development analysis of network security situation awareness technology based on UEBA. Netw. Secur. Technol. Appl. 10, 10–13 (2020)
Exabeam: User and Entity Behavior Analytics (2020). https://www.exabeam.com/siem-guide/ueba
Logrhythm: User and Entity Behavior Analytics (UEBA) (2020). http://logrhythm.com/-solutions/security/user-and-entity-behavior-analytics
Hu, S.Y.: Analysis of data leakage based on UEBA. Inf. Secur. Commun. Secur. 000(008), 26–28 (2018). (in Chinese)
Litan, A., Sadowski, G., Bussa, T.: Market guide for user and entity behavior analytics(G00349450) (2018). https://www.gartner.com/en/documents/-3872885
Nithyanantham, S., Singaravel, G.: Hybrid deep learning framework for privacy preservation in geo-distributed data centre. Intell. Autom. Soft Comput. 32(3), 1905–1919 (2022)
Dwork, C., Pottenger, R.: Toward trolling privacy. J. Am. Med. Inform. Assoc. 20(1), 102–108 (2013)
Rashid, F., Ali, M.: User and event behavior analytics on differentially private data for anomaly detection. In: 2021 7th IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), pp. 81–86. IEEE (2021)
Mo, F., Shuai, Jia, S.: Application of user entity behavior analysis technique based on machine learning in account anomaly detection. Commun. Technol. 53(05), 1262–1267 (2020)
Lei, J.: User behavior feature extraction and safety warning modeling technology. J. China Acad. Electron. Sci. 14(04), 368–372 (2019)
Mostafa, S.M.: Clustering algorithms: taxonomy, comparison, and empirical analysis in 2d datasets. J. Artif. Intell. 2(4), 189–215 (2020)
Xie, K., Wu, J.: User portrait and user behavior analysis based on big data platform. China Inf. 000(003), 100–104 (2018)
Almazroi, A.A., Sher, R.: COVID-19 cases prediction in saudi arabia using tree-based ensemble models. Intell. Autom. Soft Comput. 32(1), 389–400 (2022)
Tang, B., Hu, Q., Lin, D.: Reducing false positives of user-to-entity first-access alerts for user behavior analytics. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 804–811. IEEE (2017)
Palaniappan, L., Selvaraj, K.: Profile and rating similarity analysis for recommendation systems using deep learning. Comput. Syst. Sci. Eng. 41(3), 903–917 (2022)
Funding
This work is supported by the science and technology project of State Grid Corporation of China Funding Item: “Research on Dynamic Access Authentication and Trust Evaluation Technology of Power Mobile Internet Services Based on Zero Trust” (Grand No. 5700-202158183A-0-0-00).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that they have no conflicts of interest to report regarding the present study.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dai, Z. et al. (2022). Research on Power Mobile Internet Security Situation Awareness Model Based on Zero Trust. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-06791-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06790-7
Online ISBN: 978-3-031-06791-4
eBook Packages: Computer ScienceComputer Science (R0)