Multi-source log-based security monitoring for JDIH Kota Padang website
DOI:
https://doi.org/10.70038/jentik.v4i2.203Keywords:
server log, website security, JDIH, security monitoring, indicator prioritizationAbstract
This study develops a security indicator prioritization framework based on multi-source log correlation on the JDIH Kota Padang website. A descriptive quantitative method was applied using access logs, authentication logs, and error logs. The analysis involved parsing, event categorization, percentage calculation, visualization, and Security Event Priority Score (SEPS). The results identified 213,356 requests, 20,630 unique IP addresses, 24,315 responses with 404 status, 14,222 responses with 403 status, 80,525 failed password events, 61,393 invalid user events, and dominant directory forbidden and file not found errors. The SEPS framework supports administrators in determining security monitoring priorities.
References
E. Z. Darojat, E. Sediyono, and I. Sembiring, “Vulnerability Assessment Website E-Government dengan NIST SP 800-115 dan OWASP Menggunakan Web Vulnerability Scanner,” JURNAL SISTEM INFORMASI BISNIS, vol. 12, no. 1, pp. 36–44, Sep. 2022, doi: 10.21456/vol12iss1pp36-44.
“The NIST Cybersecurity Framework (CSF) 2.0,” Feb. 2024. doi: 10.6028/NIST.CSWP.29.
K. Kent and M. Souppaya, “Special Publication 800-92 Guide to Computer Security Log Management Recommendations of the National Institute of Standards and Technology.”
K. A. Scarfone, M. P. Souppaya, A. Cody, and A. D. Orebaugh, “Technical guide to information security testing and assessment.,” Gaithersburg, MD, 2008. doi: 10.6028/NIST.SP.800-115.
K. A. Scarfone and P. M. Mell, “Guide to Intrusion Detection and Prevention Systems (IDPS),” Gaithersburg, MD, 2007. doi: 10.6028/NIST.SP.800-94.
K. M. Firdaus and U. N. Surabaya, “JURNAL MANAJEMEN TEKNOLOGI INFORMATIKA Integrasi Faktor Manusia dalam Tata Kelola Keamanan Siber Berbasis Cloud: Studi Pengembangan Framework,” Jl. Veteran No.26B, p. 25115.
M. Du, F. Li, G. Zheng, and V. Srikumar, “DeepLog: Anomaly detection and diagnosis from system logs through deep learning,” in Proceedings of the ACM Conference on Computer and Communications Security, Association for Computing Machinery, Oct. 2017, pp. 1285–1298. doi: 10.1145/3133956.3134015.
S. He, J. Zhu, P. He, and M. R. Lyu, “Experience Report: System Log Analysis for Anomaly Detection,” in Proceedings - International Symposium on Software Reliability Engineering, ISSRE, IEEE Computer Society, Dec. 2016, pp. 207–218. doi: 10.1109/ISSRE.2016.21.
P. He, J. Zhu, Z. Zheng, and M. R. Lyu, “Drain: An Online Log Parsing Approach with Fixed Depth Tree,” in Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017, Institute of Electrical and Electronics Engineers Inc., Sep. 2017, pp. 33–40. doi: 10.1109/ICWS.2017.13.
W. Meng et al., “LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs,” 2019.
X. Zhang et al., “Robust log-based anomaly detection on unstable log data,” in ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Association for Computing Machinery, Inc, Aug. 2019, pp. 807–817. doi: 10.1145/3338906.3338931.
J. Zhu et al., “Tools and Benchmarks for Automated Log Parsing,” Jan. 2019, [Online]. Available: http://arxiv.org/abs/1811.03509
U. DigitalCommons, U. All Graduate Theses, and H. Guo, “LogBERT: Log Anomaly Detection via BERT,” 2021.
A. L. Buczak and E. Guven, “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection,” IEEE Communications Surveys and Tutorials, vol. 18, no. 2, pp. 1153–1176, Apr. 2016, doi: 10.1109/COMST.2015.2494502.
M. Ahmed, A. Naser Mahmood, and J. Hu, “A survey of network anomaly detection techniques,” Jan. 01, 2016, Academic Press. doi: 10.1016/j.jnca.2015.11.016.
A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, “Survey of intrusion detection systems: techniques, datasets and challenges,” Cybersecurity, vol. 2, no. 1, Dec. 2019, doi: 10.1186/s42400-019-0038-7.
Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, Jan. 2021, doi: 10.1002/ett.4150.
I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. Ng, “Cybersecurity data science: an overview from machine learning perspective,” J. Big Data, vol. 7, no. 1, Dec. 2020, doi: 10.1186/s40537-020-00318-5.
M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” Journal of Information Security and Applications, vol. 50, Feb. 2020, doi: 10.1016/j.jisa.2019.102419.
V. Chandola, “Anomaly Detection : A Survey,” 2009.
P. García-Teodoro, J. Díaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges,” Comput. Secur., vol. 28, no. 1–2, pp. 18–28, Feb. 2009, doi: 10.1016/j.cose.2008.08.003.
R. Sommer and V. Paxson, “Outside the Closed World: On Using Machine Learning For Network Intrusion Detection.”
N. Moustafa and J. Slay, “UNSW-NB15: A Comprehensive Data set for Network Intrusion Detection systems (UNSW-NB15 Network Data Set).” [Online]. Available: https://cve.mitre.org/
Harry Setya Hadi and Nicodemus Rahanra, “Explainable Artificial Intelligence Methods for Autonomous Robot Decision Making: A Multi Agent Framework with Safety Assurance and Ethical Constraint Optimization ”, ISR, vol. 1, no. 1, pp. 65–81, Jan. 2026.
I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in ICISSP 2018 - Proceedings of the 4th International Conference on Information Systems Security and Privacy, SciTePress, 2018, pp. 108–116. doi: 10.5220/0006639801080116.
IEEE Staff, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. I E E E, 2009.
V. Paxson, “Bro: A System for Detecting Network Intruders in Real-Time.”
H. Debar, M. Dacier, and A. Wespi, “Towards a Taxonomy o f I n trusion-Detection Systems.”
S. Axelsson, “The Base-Rate Fallacy and the Difficulty of Intrusion Detection,” 1094. [Online]. Available: http://www.ce.chalm-
H. J. Liao, C. H. Richard Lin, Y. C. Lin, and K. Y. Tung, “Intrusion detection system: A comprehensive review,” Jan. 2013. doi: 10.1016/j.jnca.2012.09.004.
M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A Survey of Network-based Intrusion Detection Data Sets,” 2019.
Setya Hadi, R. Rauf, Agus Salim, and Kevin Maulana Firdaus, “Smart Seismic Intelligence Machine Learning for Spatial Clustering and Earthquake Magnitude Prediction in Indonesia”, ZTR, vol. 8, no. 1, pp. 65–72, Mar. 2026.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Kevin Maulana Firdaus, Rahardian Bisma, Agus Salim

This work is licensed under a Creative Commons Attribution 4.0 International License.



