Performance Evaluation of A Snort-Based Intrusion Detection System in A Hospital Network

Sabrina Nur Saraswati; Muhammad Abdul Jumali

doi:10.33506/mt.v12i1.5110

Penulis

Sabrina Nur Saraswati Universitas PGRI Adi Buana Surabaya
Muhammad Abdul Jumali Universitas PGRI Adi Buana Surabaya

DOI:

https://doi.org/10.33506/mt.v12i1.5110

Kata Kunci:

Evaluasi Kinerja, Jaringan Rumah Sakit, Keamanan Jaringan, Sistem Deteksi Intrusi, Snort

Abstrak

Keamanan jaringan pada lingkungan rumah sakit menjadi tantangan kritis akibat tingginya volume trafik dan sensitivitas data medis. Penelitian ini bertujuan mengevaluasi kinerja Intrusion Detection System (IDS) berbasis Snort dalam mendeteksi serangan jaringan pada infrastruktur Rumah Sakit Mitra Keluarga. Evaluasi dilakukan melalui implementasi Snort pada segmen jaringan server menggunakan metode eksperimen dengan simulasi serangan berupa port scanning, brute force SSH, ICMP flooding, dan SQL injection. Kinerja sistem diukur berdasarkan waktu respon deteksi, tingkat keberhasilan deteksi, serta konsistensi alert yang dihasilkan. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi seluruh skenario serangan dengan waktu respon antara 0,4–1 detik dan tingkat deteksi 100% pada skenario pengujian. Namun, ditemukan potensi false positive pada trafik ICMP internal yang memerlukan penyesuaian parameter threshold. Temuan ini menunjukkan bahwa IDS berbasis Snort efektif digunakan sebagai mekanisme deteksi awal serangan pada jaringan rumah sakit dan dapat ditingkatkan melalui integrasi dengan sistem monitoring terpusat untuk mendukung pengambilan keputusan keamanan jaringan.

Referensi

Alshamrani, A., Chowdhary, A., Al-Bogami, N., & Huang, D. (2020). A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities. IEEE Communications Surveys & Tutorials, 22(4), 2349–2376. https://doi.org/10.1109/COMST.2020.2977661

Behl, A., & Behl, K. (2017). Cyberwar: The Next Threat to National Security and What to Do About It. Oxford University Press.

Cisco. (2023). Snort 3 User Manual. Cisco Systems.

Daah, C., Qureshi, A., Awan, I., & Konur, S. (2025). Simulation-based evaluation of advanced threat detection and response in financial industry networks using zero trust and blockchain technology. Simulation Modelling Practice and Theory, 138, 103027. https://doi.org/https://doi.org/10.1016/j.simpat.2024.103027

Diana, L., Dini, P., & Paolini, D. (2025). Overview on Intrusion Detection Systems for Computers Networking Security. In Computers (Vol. 14, Issue 3, p. 87). https://doi.org/10.3390/computers14030087

ENISA. (2023). Threat Landscape for Health Sector. European Union Agency for Cybersecurity.

Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. https://doi.org/10.1016/j.jisa.2019.102419

Jeffrey, N., Tan, Q., & Villar, J. R. (2023). A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems. In Electronics (Vol. 12, Issue 15, p. 3283). https://doi.org/10.3390/electronics12153283

Jumali, M. A. (2018). Pengukuran kinerja sistem operasional berbasis indikator kuantitatif. Jurnal Teknik Industri, 19(2), 101–110.

Jumali, M. A., & Pratama, R. (2021). Continuous improvement pada sistem layanan berbasis pengukuran kinerja. Jurnal Sistem Dan Manajemen Industri, 5(1), 45–56.

Jumali, M. A., Santoso, B., & Lestari, D. (2023). Pengendalian variabilitas pada sistem sosio-teknis. International Journal of Industrial Engineering and Management, 14(3), 201–214.

Kampourakis, K. E., Gkioulos, V., Kavallieratos, G., & Lin, J.-C. (2025). Digital Twin-Enabled Incident Detection and Response: A Systematic Review of Critical Infrastructures Applications. International Journal of Information Security, 24(5), 194. https://doi.org/10.1007/s10207-025-01113-0

Kumar, R., & Tripathi, R. (2021). Secure healthcare systems using intrusion detection techniques. Computer Communications, 172, 95–108. https://doi.org/10.1016/j.comcom.2021.03.005

Llopis Sanchez, S., Sandoval Rodriguez-Bermejo, D., Daton Medenou, R., Pasqual de Riquelme, R., Torelli, F., & Maestre Vidal, J. (2022). Tackling Verification and Validation Techniques to Evaluate Cyber Situational Awareness Capabilities. In Mathematics (Vol. 10, Issue 15, p. 2617). https://doi.org/10.3390/math10152617

Mohamed, N. (2025). Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms. Knowledge and Information Systems, 67(8), 6969–7055. https://doi.org/10.1007/s10115-025-02429-y

Neely, A., Gregory, M., & Platts, K. (2015). Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, 25(12), 1228–1263. https://doi.org/10.1108/01443570510633639

NIST. (2024). Cybersecurity Framework 2.0. National Institute of Standards and Technology.

Sarker, I. H., Abushark, Y., Alsolami, F., & Khan, A. (2020). Intrusion detection systems in healthcare: A review. IEEE Access, 8, 115563–115587. https://doi.org/10.1109/ACCESS.2020.3003721

Scarfone, K., & Mell, P. (2019). Guide to Intrusion Detection and Prevention Systems (IDPS). National Institute of Standards and Technology.

Security, I. B. M. (2023). Cost of a Data Breach Report. IBM Corporation.

Shone, N., Ngoc, T., Phai, V., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50. https://doi.org/10.1109/TETCI.2017.2772792

Smiliotopoulos, C., Kambourakis, G., & Kolias, C. (2024). Detecting lateral movement: A systematic survey. Heliyon, 10(4), e26317. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e26317

Sommer, R., & Paxson, V. (2019). Outside the closed world: On using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy, 305–316. https://doi.org/10.1109/SP.2010.25

Sophos. (2024). The State of Ransomware in Healthcare. Sophos Ltd.

Szynkiewicz, P. (2022). Signature-Based Detection of Botnet DDoS Attacks (pp. 120–135). https://doi.org/10.1007/978-3-031-04036-8_6

Zhang, Y., & Wei, J. (2022). Network intrusion detection for hospital information systems. Journal of Medical Systems, 46(3), 1–12. https://doi.org/10.1007/s10916-022-01834-9