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Abstract
The implementation of a Web Application Firewall (WAF) based on the OWASP Core Rule Set (CRS) aims to enhance web application security; however, improper configuration may lead to false positives that adversely affect system performance and service availability. This study analyzes the impact of WAF false positives on Open Journal Systems (OJS) services deployed in a cloud environment using a server log analysis approach. The data were collected from web server error logs and ModSecurity audit logs that recorded the blocking of legitimate requests during the manuscript submission process due to inbound anomaly scores exceeding predefined security thresholds. The results indicate that WAF false positives caused service failures characterized by HTTP 403 responses, increased submission errors, and a measurable reduction in OJS service availability during the observation period. These findings demonstrate that anomaly-based detection mechanisms in OWASP CRS may misclassify normal application behavior as malicious activity. This study provides empirical evidence based on server logs regarding the impact of WAF false positives on cloud service reliability and offers insights for WAF policy tuning to achieve a balance between security and service availability.
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References
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References
Ank Shah, J.K., D Janani, E.A., Rajashree Sutrawe, 2025. CYBER THREAT DETECTION AND PROFILING USING AI. ResearchGate. https://doi.org/10.55041/IJSREM.NCFT025
Dawadi, B.R., Adhikari, B., Srivastava, D.K., Dawadi, B.R., Adhikari, B., Srivastava, D.K., 2023. Deep Learning Technique-Enabled Web Application Firewall for the Detection of Web Attacks. Sensors 23. https://doi.org/10.3390/s23042073
Díaz-Verdejo, J., Muñoz-Calle, J., Alonso, A.E., Alonso, R.E., Madinabeitia, G., Díaz-Verdejo, J., Muñoz-Calle, J., Alonso, A.E., Alonso, R.E., Madinabeitia, G., 2022. On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks. Appl. Sci. 12. https://doi.org/10.3390/app12020852
Floris, G., Scano, C., Montaruli, B., Demetrio, L., Valenza, A., Compagna, L., Ariu, D., Piras, L., Balzarotti, D., Biggio, B., 2025. ModSec-AdvLearn: Countering Adversarial SQL Injections With Robust Machine Learning. IEEE Trans. Inf. Forensics Secur. 20, 6693–6705. https://doi.org/10.1109/TIFS.2025.3583234
MajedA.Alowaidi, S., Sunil Kumar Sharma, 2025. Impact of security standards and policies on the credibility of e-government | Request PDF. ResearchGate. https://doi.org/10.1007/s12652-020-02767-5
Ott, H., Bogatinovski, J., Acker, A., Nedelkoski, S., Kao, O., 2021. Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models. https://doi.org/10.48550/arXiv.2102.11570
OWASP CRS | OWASP Foundation [WWW Document], n.d. URL https://owasp.org/www-project-modsecurity-core-rule-set/ (accessed 12.17.25).
Ravindran, V.K., Ojha, S.S., Cambodia, A., 2025. A Comparative Analysis of Signature-Based and Anomaly-Based Intrusion Detection Systems. Int. J. Latest Technol. Eng. Manag. Appl. Sci. 14, 209–214. https://doi.org/10.51583/IJLTEMAS.2025.140500026
Reyes Narváez, A., Curipallo Martínez, M., Reyes Narváez, E., Lara, F., Reyes Narváez, E.P., Barba Molina, H., 2025. Evaluation Framework for False Positives in Open-Source WAFs Based on OWASP CRS Paranoia Levels: A Systematic Approach for Comparative Measurement. Eng. Proc. 115, 1. https://doi.org/10.3390/engproc2025115001
Riadi, I., Yudhana, A., W, Y., 2020. The security analysis of the Open Journal System website uses the vulnerability assessment method. J. Techno. Inf. and Computing Science. 7, 853–860. https://doi.org/10.25126/jtiik.2020701928
Scano, C., Floris, G., Montaruli, B., Demetrio, L., Valenza, A., Compagna, L., Ariu, D., Piras, L., Balzarotti, D., Biggio, B., 2025. ModSec-Learn: Boosting ModSecurity with Machine Learning, in: Mehmood, R., Hernández, G., Praça, I., Wikarek, J., Loukanova, R., Monteiro dos Reis, A., Skarmeta, A., Lombardi, E. (Eds.), Distributed Computing and Artificial Intelligence, Special Sessions I, 21st International Conference. Springer Nature Switzerland, Cham, pp. 23–33.
Siwach, M., Mann, D.S., 2022. Anomaly Detection for Web Log Data Analysis: A Review. J. Algebr. Stat. 13.
Utama, F.P., Nurhadi, R.M.H., 2024. Uncovering the Risk of Academic Information System Vulnerability through PTES and OWASP Method. Common CommIT. Inf. Technol. J. 18, 39–51. https://doi.org/10.21512/commit.v18i1.9384
Viradia, V., Jain, A., Ogety, S.S., Donvir, A., 2025. Resilient Cloud Computing Systems for Mission-Critical Applications, in: 2025 IEEE International Conference on Electro Information Technology (eIT). Presented at the 2025 IEEE International Conference on Electro Information Technology (eIT), pp. 311–315. https://doi.org/10.1109/eIT64391.2025.11103702
Zhou, Y., Zhang, S., Cui, X., Zhang, C., Li, X., 2021. An Accurate Torque Output Method for Open-End Winding Permanent Magnet Synchronous Motors Drives. IEEE Trans. Energy Converse. 36, 3470–3480. https://doi.org/10.1109/TEC.2021.3083958