Tinjauan Sistematis terhadap Ensemble SemiSupervised Network Intrusion Detection Systemdengan Perspektif Adversarial Robustness

  • Sofy Fitriani Politeknik Negeri Bandung ORCID
  • Siti Dwi Setiarini Politeknik Negeri Bandung
  • Muhammad Irfan Nurdin Politeknik Negeri Bandung
  • Ridwan Nurdin Politeknik Negeri Bandung
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DOI https://doi.org/10.31544/jtera.v11.i1.2026.209-118
Published 2026-06-30
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Abstract

Sistem deteksi intrusi jaringan atau Network Intrusion Detection System (NIDS) berbasis pembelajaran mesin
menjadi pilar utama keamanan siber modern. Namun, model berbasis data berlabel penuh sering kali terbatas dalam
generalisasi dan rentan terhadap serangan adversarial. Pendekatan semi-supervised learning memanfaatkan data tak
berlabel untuk memperluas cakupan pelatihan, sementara ensemble learning meningkatkan stabilitas melalui
keragaman model. Penelitian ini melakukan Systematic Literature Review (SLR) untuk menganalisis tren, arah
perkembangan, dan research gap pada kombinasi ensemble semi-supervised NIDS, dengan fokus utama pada
robustness terhadap serangan adversarial. Proses review mengikuti pedoman PRISMA dengan sumber data dari IEEE
Xplore, Scopus, SpringerLink, dan ScienceDirect untuk periode 2018–2025. Dari 147 artikel awal, 65 memenuhi
kriteria inklusi. Hasil analisis menunjukkan sebagian besar penelitian menitikberatkan peningkatan akurasi (F1 >
95%), namun hanya 18% yang menguji ketahanan terhadap serangan adversarial secara eksplisit. Belum ditemukan
studi yang mengintegrasikan ensemble multi-paradigma, semi-supervised learning, dan evaluasi adversarial attack
secara terpadu. Kontribusi utama penelitian ini adalah penyusunan kerangka konseptual baru, Adversarial-Aware
Hybrid Ensemble Framework, yang menempatkan proses adversarial design sebagai bagian integral dari pelatihan
semi-supervised ensemble NIDS untuk meningkatkan keandalan sistem deteksi intrusi modern.

Keywords

Network Intrusion Detection System Ensemble Learning Semi-Supervised Learning Adversarial Attack Systematic Literature Review

References

Ghosh, S., & Sharma, A. (2021). An Ensemble-Based Intrusion Detection System for Cloud Networks. IEEE Access, 9, 1328–1337.
Patcha, M., & Park, J. (2020). An Overview of Anomaly Detection Techniques: Existing Solutions and Latest Technological Trends. Computers & Security, 28(1), 18–28.
Zhao, L., Wang, H., & Xu, Y. (2020). Deep Ensemble Learning for Network Intrusion Detection. Information Fusion, 66, 80–90.
Uthradevi, R., Nithya, K., & Sridevi, R. (2025). Semi-Supervised Deep Learning for IoT Intrusion Detection. Journal of Network and Computer Applications, 224, 103743.
Chapelle, O., Schölkopf, B., & Zien, A. (2020). Semi-Supervised Learning. MIT Press.
Miyato, T., Maeda, S., Koyama, M., & Ishii, S. (2019). Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8), 1979–1993.
Kim, M., Kim, J., & Park, H. (2022). MANomaly: Mutual Adversarial Networks for Semi-Supervised Anomaly Detection. Information Sciences, 602, 55–72.
Uthradevi, R., Nithya, K., & Sridevi, R. (2025). Hybrid Semi-Supervised Intrusion Detection in IoT Systems. Journal of Network and Computer Applications, Springer.
Noor, A., Ridzuan, M., & Chien, H. (2025). Semi-Supervised Intrusion Detection Model for IoT-Based Systems. Symmetry, 17(3), 905.
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards Deep Learning Models Resistant to Adversarial Attacks. In Proc. International Conference on Learning Representations (ICLR).
Goodfellow, I., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. In Proc. International Conference on Learning Representations (ICLR).
Lin, W., Xu, W., & Zhang, J. (2020). IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection. IEEE Access, 8, 65420–65434.
Zhang, K., Ren, L., & Han, J. (2024). Adversarial Attacks and Defenses in Deep Learning-Based Network Intrusion Detection Systems. Computers & Security, 130, 103290.
Sharma, A., & Chen, C. (2024). A Systematic Study of Adversarial Attacks Against ML-Based NIDS. Electronics, 13(2), 252.
Al-Atwi, A., & Morisset, J. (2021). Adversarial Machine Learning in Network Intrusion Detection Systems: A Systematic Review. arXiv preprint, arXiv:2109.08821.
Shu, Y., Li, H., & Wang, Y. (2020). OMNI: Automated Ensemble with Unexpected Models Against Adversarial Attack. In Proc. AAAI Conference on Artificial Intelligence, pp. 533–540.
Kalpani, D., Wijesundara, N., & Samarasinghe, P. (2025). Cutting-Edge Approaches in Intrusion Detection Systems: A Systematic Review. SN Computer Science, 6(4), 456.
Ren, L., Li, X., & Zhang, W. (2025). ADHS-EL: Dynamic Ensemble Learning with Adversarial Augmentation for Robust Network Intrusion Detection. Journal of Network Security, 5(2), 122–135.
Mutembei, B., Ombeni, F. A., & Mutembei, C. K. (2024). Deep Learning-Based Network Intrusion Detection Systems: A Systematic Literature Review. In Lecture Notes in Computer Science (LNCS), Springer.
Vega, R., Martinez, J., & Lopez, F. (2025). Ensemble Approaches for Multi-Class Intrusion Detection Systems: A Review. Future Internet, 17(2), 98.
Kaur, E., Singh, P., & Kaur, M. (2023). Systematic Review of Intrusion Detection System Research Challenges. De Gruyter Journal of Information Security, 14(1), 45–60.
Hossain, M., Rahman, S., & Aziz, A. (2023). Semi-Supervised Autoencoder for Robust Network Intrusion Detection. In Proc. National Symposium on System Security (NSysS).
Qiao, J., Zhang, X., & Chen, L. (2023). SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection Systems. Computers & Security, 138, 103812.

Issue

Vol. 11 No. 1: June 2026

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How to Cite

[1]
“Tinjauan Sistematis terhadap Ensemble SemiSupervised Network Intrusion Detection Systemdengan Perspektif Adversarial Robustness”, JTERA, vol. 11, no. 1, pp. 209–118, Jun. 2026, doi: 10.31544/jtera.v11.i1.2026.209-118.