Advanced Persistent Threats Analysis and Intrusion Detection Systems Evaluation

Authors

DOI:

https://doi.org/10.35870/ijsecs.v5i3.5770

Keywords:

APT, SIEM, Wazuh, Advanced Persistent Threat, Intrusion Detection, Cybersecurity

Abstract

-

Advanced Persistent Threats are significant cybersecurity threats that employ covert and strategically planned operations to achieve long-term unauthorized access and data exfiltration. PT XYZ, a logistics company with considerable operational and customer data, is more susceptible to APTs, which is why the company decided to implement Wazuh as an open-source SIEM platform for improved intrusion detection capabilities. We assessed how effectively this IDS-SIEM implementation could detect and respond to APT scenarios by analyzing multi-source logs from Wazuh, Sysmon, and endpoint telemetry across PT XYZ’s PC infrastructure between June 3-30, 2025—capturing 35,333 records in total. Simulated APT attacks were carried out using Atomic Red Team with detection mapping based on MITRE ATT&CK tactics. Most of the early stages of attack phases were identified by Wazuh particularly Initial Access and Execution phases where the system logged 1,060 true positives; 8,537 true negatives; 563 false positives; and 440 false negatives at an accuracy rate of 91%. Normal traffic detection results were good with a precision of 0.95, recall of 0.94 F1-score at the same value whereas attack detection had a precision value of 0.65 with a recall of 0.71 giving it an F1 score of 0.68 making macro-averaged metrics fall at values such as 0.80 for precision and 0.82 for recall which further brought the F1 score up to 0.81 while weighted averages peaked at 0.91.Our results indicate that an open-source SIEM like Wazuh can be used effectively for the detection of APTs in logistics operations when configured appropriately using MITRE ATT&CK-based threat simulations – hence having real-world applicability towards improving cybersecurity defenses within this sector.

Downloads

Download data is not yet available.

Author Biographies

  • Dedy Wibowo, Universitas Pamulang

    Postgraduate, Master of Informatics Engineering, Universitas Pamulang, South Tangerang City, Banten Province, Indonesia

  • Taswanda Taryo, Universitas Pamulang

    Postgraduate, Master of Informatics Engineering, Universitas Pamulang, South Tangerang City, Banten Province, Indonesia

  • Ferhat Aziz, Universitas Pamulang

    Postgraduate, Master of Informatics Engineering, Universitas Pamulang, South Tangerang City, Banten Province, Indonesia

References

Santoso, J. T., Hartono, B., Silalahi, F. D., & Muthohir, M. (2024). Transformers in cybersecurity: Advancing threat detection and response through machine learning architectures. Journal of Technology Informatics and Engineering, 3(3), 382–396. https://doi.org/10.51903/jtie.v3i3.211

Virkud, A., Inam, M. A., Riddle, A., Liu, J., Wang, G., & Bates, A. (2024). How does endpoint detection use the MITRE ATT&CK framework? https://www.usenix.org/conference/usenixsecurity24/presentation/virkud

Artioli, P., Dentamaro, V., Galantucci, S., Magrì, A., Pellegrini, G., & Semeraro, G. (2025). SIEVE: Generating a cybersecurity log dataset collection for SIEM event classification. Computer Networks, 266, 111330. https://doi.org/10.1016/j.comnet.2025.111330

Lu, S., Chi, B., Zhou, T., Zhou, W., & Hu, H. (2025). STAE-APT: An APT detection method based on long-term behavioral features from provenance graphs. 2025 International Conference on Smart Computing and Artificial Intelligence Technology, 1834–1841. https://doi.org/10.1109/iscait64916.2025.11010360

Jang, S.-W., & Lee, Y.-J. (2023). A study on the APT attack scenario verification system. Journal of the Korea Academia-Industrial Cooperation Society, 24(4), 610–615. https://doi.org/10.5762/kais.2023.24.4.610

Mamun, A. Al, Al-Sahaf, H., Welch, I., & Camtepe, S. (2025). Genetic programming for enhanced detection of advanced persistent threats through feature construction. Computers and Security, 149, 104185. https://doi.org/10.1016/j.cose.2024.104185

Andronache, M.-M., Vulpe, A., & Burileanu, C. (2025). A comparative study of intrusion events in different SIEM systems. 2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI), 000065–000070. https://doi.org/10.1109/SAMI63904.2025.10883178

Radoglou-Grammatikis, P., Sarigiannidis, P., Iturbe, E., Rios, E., Martinez, S., Sarigiannidis, A., Eftathopoulos, G., Spyridis, Y., Sesis, A., Vakakis, N., Tzovaras, D., Kafetzakis, E., Giannoulakis, I., Tzifas, M., Giannakoulias, A., Angelopoulos, M., & Ramos, F. (2021). SPEAR SIEM: A security information and event management system for the smart grid. Computer Networks, 193, 108008. https://doi.org/10.1016/j.comnet.2021.108008

Ayu, M. A., Erlangga, D., Mantoro, T., & Handayani, D. (2023). Enhancing security information and event management (SIEM) by incorporating machine learning for cyber attack detection. 2023 IEEE 9th International Conference on Computing, Engineering and Design, ICCED 2023. https://doi.org/10.1109/ICCED60214.2023.10425288

Nas, M., Ulfiah, F., Putri, U., Elektro, T., Negeri, P., & Pandang, U. (2023). Analisis sistem security information and event management (SIEM) aplikasi Wazuh pada Dinas Komunikasi Informatika Statistik dan Persandian Sulawesi Selatan. Jurnal Teknologi Elekterika, 20(2). https://doi.org/10.31963/elekterika.v20i2.4536

Vazão, A. P., Santos, L., Costa, R. L. de C., & Rabadão, C. (2023). Implementing and evaluating a GDPR-compliant open-source SIEM solution. Journal of Information Security and Applications, 75, 103509. https://doi.org/10.1016/j.jisa.2023.103509

Ahmad, S., Ahn, B., Alvee, S. R. B., Trevino, D., Kim, T., Youn, Y. W., & Ryu, M. H. (2022, April). Advanced persistent threat (APT)-style attack modeling and testbed for power transformer diagnosis system in a substation. 2022 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2022. https://doi.org/10.1109/ISGT50606.2022

Karim, S. S., Afzal, M., Iqbal, W., & Al Abri, D. (2024). Advanced persistent threat (APT) and intrusion detection evaluation dataset for Linux systems 2024. https://doi.org/10.17632/5x68fv63sh.2

Park, N. E., Lee, Y. R., Joo, S., Kim, S. Y., Kim, S. H., Park, J. Y., Kim, S. Y., & Lee, I. G. (2023). Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks. Computers and Electrical Engineering, 105, 108548. https://doi.org/10.1016/j.compeleceng.2022.108548

Sheng, C., & Gang, C. (2024). APT attack and detection technology. IMCEC 2024 - IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference, 795–801. https://doi.org/10.1109/IMCEC59810.2024.10575432

Ward, A. M., Bohacek, S., & Phillips, J. (2024). An evaluation study of endpoint detection and response systems across multi-vector attack scenarios. Eastern-European Journal of Enterprise Technologies, 2(9), 182–191. https://doi.org/10.30837/2522-9818.2024.2.182

Alexopoulos, A., & Daras, N. J. (2020). Mathematical study of advanced persistent threat (APT) hunting techniques. Journal of Computations & Modelling, 10(2), 1–24. https://doi.org/10.47260/jcomod/1021

Cheng, S. M., Lui, Y. C., Tsai, N. J., & Hong, B. K. (2024). Toward intelligent IoT endpoint detection and response using digital twins via firmware emulation. IEEE Internet of Things Magazine, 7(6), 20–26. https://doi.org/10.1109/IOTM.001.2400070

Gulbay, B., & Demirci, M. (2024). APT-scope: A novel framework to predict advanced persistent threat groups from enriched heterogeneous information network of cyber threat intelligence. Engineering Science and Technology, an International Journal, 57, 101791. https://doi.org/10.1016/j.jestch.2024.101791

Mahmoud, R. V., Anagnostopoulos, M., Pastrana, S., & Pedersen, J. M. (2024). Redefining malware sandboxing: Enhancing analysis through Sysmon and ELK integration. IEEE Access, 12, 68624–68636. https://doi.org/10.1109/ACCESS.2024.3400167

Muhammad, A. R., Sukarno, P., & Wardana, A. A. (2022). Integrated security information and event management (SIEM) with intrusion detection system (IDS) for live analysis based on machine learning. Procedia Computer Science, 217, 1406–1415. https://doi.org/10.1016/j.procs.2022.12.339

Artioli, P., Dentamaro, V., Galantucci, S., Magrì, A., Pellegrini, G., & Semeraro, G. (2025). SIEVE: Generating a cybersecurity log dataset collection for SIEM event classification. Computer Networks, 266, 111330. https://doi.org/10.1016/j.comnet.2025.111330

Chi, C. H., Ooi, S. Y., Binti, E. H., Pang, Y. H., Yan, M. K. B. A., & Sidin, K. I. B. (2023). Intelligent-based SIEM security email alert. 2023 11th International Conference on Information and Communication Technology, ICoICT 2023, 481–486. https://doi.org/10.1109/ICoICT58202.2023.10262562

Esseghir, A., Kamoun, F., & Hraiech, O. (2022). AKER: An open-source security platform integrating IDS and SIEM functions with encrypted traffic analytic capability. Journal of Cyber Security Technology, 6(1–2), 27–64. https://doi.org/10.1080/23742917.2022.2058836

Sinaga, Y. Y. (2024). Analisis security information and event management (SIEM) berbasis Wazuh dalam mendeteksi malicious software pada sistem operasi Linux. Universitas Sumatera Utara. https://repositori.usu.ac.id/handle/123456789/96053

Cheng, S. M., Lui, Y. C., Tsai, N. J., & Hong, B. K. (2024). Toward intelligent IoT endpoint detection and response using digital twins via firmware emulation. IEEE Internet of Things Magazine, 7(6), 20–26. https://doi.org/10.1109/IOTM.001.2400070

Amami, R., Charfeddine, M., & Masmoudi, S. (2024). Exploration of open source SIEM tools and deployment of an appropriate Wazuh-based solution for strengthening cyberdefense. 10th 2024 International Conference on Control, Decision and Information Technologies, CoDIT 2024, 2139–2145. https://doi.org/10.1109/CoDIT62066.2024.10708476

Tharunika, V. S., Shridhar, T., Veeresh, K., Thangavel, S. K., Srinivasan, K., Vajipayajula, S., & Tibrewal, A. (2023). Detection and prevention of advanced persistent threat (APT) activities in heterogeneous networks using SIEM and deep learning. 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023. https://doi.org/10.1109/ICCCNT56998.2023.10306968

Saeed, N., Yaqub, M., Haider, A., Secureworks, D., Safdar, S., & Khan, H. (2025). An enhanced mechanism for advanced persistent threat (APT) detection based on deep learning. Spectrum of Engineering Sciences, 3(1), 48–62. https://sesjournal.com/index.php/1/article/view/118

Ren, W., Song, X., Hong, Y., Lei, Y., Yao, J., Du, Y., & Li, W. (2023). APT attack detection based on graph convolutional neural networks. International Journal of Computational Intelligence Systems, 16(1), 168. https://doi.org/10.1007/s44196-023-00369-5

Downloads

Published

2025-12-01

How to Cite

Wibowo, D., Taryo, T., & Aziz, F. (2025). Advanced Persistent Threats Analysis and Intrusion Detection Systems Evaluation. International Journal Software Engineering and Computer Science (IJSECS), 5(3), 1070-1082. https://doi.org/10.35870/ijsecs.v5i3.5770

Most read articles by the same author(s)