LINEAR REGRESSION FOR PREDICTION OF EXCESSIVE PERMISSIONS DATABASE ACCOUNT TRAFFIC

Agus Pamuji; Heri Satria Setiawan

doi:10.20884/1.jutif.2022.3.2.206

Agus Pamuji Bimbingan Konseling Islam, Fakultas Ushuluddin Adab dan Dakwah, IAIN Syekh Nurjati Cirebon, Indonesia
Heri Satria Setiawan Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Indraprasta PGRI, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.2.206

Keywords: Estimation, Excessive Permissions, User Accounts, Database, Data Mining

Abstract

Today, the security of information and data is an important asset for everyone in protecting data. Data and information become critical when weaknesses and threats come. In this study, an estimation of the observed variables will be carried out. The application of data mining, especially with the estimation method by using linear regression techniques. The next stage is data preparation by referring to the dataset recorded in the user activity log. Data preparation takes a lot of time because you have to make sure the data fits the needs of data mining analysis. The analysis technique with linear regression involves three independent variables as Type Permissions, Type of User Account, Status, and the dependent variable, namely User Actions. The strongest effect was found in type_permissions and state when together on user_actions. The type_permissions variable keeps increasing when the state on the user is active. The status attribute also suffers from the same condition. Accrording to the results, our findings in root mean squared error is 37.614 and absolute error is 31.058, and mean absolute percentage about 23%. Furthermore, User_action as an estimated variable gives two data opportunities whether it is allowed or not. Therefore, in future research, it is necessary to map users of the database system still in the context of data mining when digging for information on excessive permissions.

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References

P. Menard, G. J. Bott, and R. E. Crossler, “User Motivations in Protecting Information Security: Protection Motivation Theory Versus Self-Determination Theory,” J. Manag. Inf. Syst., vol. 34, no. 4, pp. 1203–1230, 2017, doi: 10.1080/07421222.2017.1394083.

R. Diesch, M. Pfaff, and H. Krcmar, “A comprehensive model of information security factors for decision-makers,” Comput. Secur., vol. 92, no. 3, pp. 1–21, 2020, doi: 10.1016/j.cose.2020.101747.

N. Miloslavskaya and A. Tolstoy, “Internet of Things: information security challenges and solutions,” Cluster Comput., vol. 22, no. 1, pp. 103–119, 2019, doi: 10.1007/s10586-018-2823-6.

J. Norbekov, “Ensuring information security as an ideological problem,” Ment. Enlight. Sci. J., vol. 2020, no. 1, pp. 56–65, 2020.

D. Tse, B. Zhang, Y. Yang, C. Cheng, and H. Mu, “Blockchain application in food supply information security,” in IEEE International Conference on Industrial Engineering and Engineering Management, 2018, vol. 2017-Decem, pp. 1357–1361, doi: 10.1109/IEEM.2017.8290114.

M. D. McLaughlin and J. Gogan, “Challenges and best practices in information security management,” MIS Q. Exec., vol. 17, no. 3, pp. 237–262, 2018.

E. Bertino, M. Kantarcioglu, C. G. Akcora, S. Samtani, S. Mittal, and M. Gupta, “AI for Security and Security for AI,” CODASPY 2021 - Proc. 11th ACM Conf. Data Appl. Secur. Priv., pp. 333–334, 2021, doi: 10.1145/3422337.3450357.

M. Y. Alyousef and N. T. Abdelmajeed, “Dynamically detecting security threats and updating a signature-based intrusion detection system’s database,” in Procedia Computer Science, 2019, vol. 159, pp. 1507–1516, doi: 10.1016/j.procs.2019.09.321.

Y. A. Basallo, “Artificial Intelligence Techniques for Information Security Risk Assessment,” IEEE Lat. Am. Trans., vol. 16, no. 3, pp. 3–7, 2018.

J. Chevalier and D. Buckles, Participatory Action Research. 2019.

S. S. Sarmah, “Database Security – Threats & Prevention,” Int. J. Comput. Trends Technol., vol. 67, no. 5, pp. 46–53, 2019.

W. C. Alisawi, A. A. A. Hussain, and W. A. Alawsi, “Estimate new model of system management for database security,” Indones. J. Electr. Eng. Comput. Sci., vol. 14, no. 3, pp. 1391–1394, 2019, doi: 10.11591/ijeecs.v14.i3.pp1391-1394.

R. A. Teimoor, “A Review of Database Security Concepts, Risks, and Problems,” UHD J. Sci. Technol., vol. 5, no. 2, pp. 38–46, 2021, doi: 10.21928/uhdjst.v5n2y2021.pp38-46.

K. Jonah, M. Elem, N. Elem, C. Obinna, and N. Chiemezuo, “Online Database Security Threats and Solutions: The NetFlix Incident,” Int. J. Innov. Sci. Res. Technol., vol. 5, no. 12, pp. 1–6, 2020, [Online]. Available: www.ijisrt.com.

J. H. Park, S. M. Yoo, I. S. Kim, and D. H. Lee, “Security Architecture for a Secure Database on Android,” IEEE Access, vol. 6, pp. 11482–11501, 2018, doi: 10.1109/ACCESS.2018.2799384.

M. Miao, Y. Wang, J. Wang, and X. Huang, “Verifiable database supporting keyword searches with forward security,” Comput. Stand. Interfaces, vol. 77, p. 103491, 2021, doi: 10.1016/j.csi.2020.103491.

G. Chen, “Security precautionary technology for enterprise information resource database based on genetic algorithm in age of big data,” J. Comput. Methods Sci. Eng., vol. 20, no. 2, pp. 427–434, 2020, doi: 10.3233/JCM-193874.

A. Motro, “A unified model for security and integrity in relational databases,” J. Comput. Secur., vol. 1, no. 2, pp. 189–213, 1992, doi: 10.3233/JCS-1992-1204.

J. Pek, O. Wong, and C. M. Wong, “Data Transformations for Inference with Linear Regression: Clarifications and Recommendations,” Pract. Assessment, Res. Eval., vol. 22, no. 9, pp. 1–11, 2017.

A. Jozaghi et al., “Multi-model streamflow prediction using conditional bias-penalized multiple linear regression,” Stoch. Environ. Res. Risk Assess., vol. 35, no. 11, pp. 2355–2373, 2021, doi: 10.1007/s00477-021-02048-3.

L. Zhou, Y. Zhu, and K. K. R. Choo, “Efficiently and securely harnessing cloud to solve linear regression and other matrix operations,” Futur. Gener. Comput. Syst., vol. 81, no. 2, pp. 404–413, 2018, doi: 10.1016/j.future.2017.09.031.

A. F. Schmidt and C. Finan, “Linear regression and the normality assumption,” J. Clin. Epidemiol., vol. 98, no. 10, pp. 146–151, 2018, doi: 10.1016/j.jclinepi.2017.12.006.

Z. Jin, Y. Cui, and Z. Yan, “Survey of intrusion detection methods based on data mining algorithms,” PervasiveHealth Pervasive Comput. Technol. Healthc., no. 2, pp. 98–106, 2019, doi: 10.1145/3341620.3341632.

J. Zhao, S. Fang, and P. Jin, “Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM , Trust and Attitude,” J. Sustain., vol. 10, no. 356, pp. 1–26, 2018, doi: 10.3390/su10020356.

P. Zeng, G. Lin, L. Pan, Y. Tai, and J. Zhang, “Software vulnerability analysis and discovery using deep learning techniques: A survey,” IEEE Access, vol. 8, no. 4, pp. 197158–197172, 2020, doi: 10.1109/ACCESS.2020.3034766.

V. R. Balpande and R. D. Wajgi, “Prediction and severity estimation of diabetes using data mining technique,” IEEE Int. Conf. Innov. Mech. Ind. Appl. ICIMIA 2017 - Proc., no. Icimia, pp. 576–580, 2017, doi: 10.1109/ICIMIA.2017.7975526.

M. A. Meena, “Data Mining Techniques Used in Cyber Security,” Int. J. Futur. Revolut. Comput. Sci. Commun. Eng., vol. 4, no. 11, pp. 11–19, 2018, [Online]. Available: http://www.ijfrcsce.org.

C. Bergmeir, R. J. Hyndman, and B. Koo, “A note on the validity of cross-validation for evaluating autoregressive time series prediction,” Comput. Stat. Data Anal., vol. 120, no. xxxx, pp. 70–83, 2018, doi: 10.1016/j.csda.2017.11.003.

R. C. Sharma, K. Hara, and H. Hirayama, “A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data,” Scientifica (Cairo)., vol. 2017, 2017, doi: 10.1155/2017/9806479.

A. A. Argasah and D. Gustian, “DATA MINING ANALYSIS TO DETERMINE EMPLOYEE SALARIES ACCORDING TO NEEDS BASED ON THE K-MEDOIDS CLUSTERING ALGORITHM”, J. Tek. Inform. (JUTIF), vol. 3, no. 1, pp. 29-36, Feb. 2022.

Y. I. Kurniawan, A. Fatikasari, M. L. Hidayat, and M. Waluyo, “PREDICTION FOR COOPERATIVE CREDIT ELIGIBILITY USING DATA MINING CLASSIFICATION WITH C4.5 ALGORITHM ”, J. Tek. Inform. (JUTIF), vol. 2, no. 2, pp. 67-74, Mar. 2021