Systematic Literature Review: Artificial Intelligence untuk Klasifikasi pada Sistem Manajemen Jaringan Berbasis Web

Muhammad Rifqi Fadhlan Rahmatullah; Hana Kurnia Putri; Candhy Fadhila Arsyad

doi:10.55382/jurnalpustakarobotsister.v4i2.2253

Penulis

Muhammad Rifqi Fadhlan Rahmatullah Universitas Doktor Nugroho Magetan
Hana Kurnia Putri Universitas Doktor Nugroho Magetan
Candhy Fadhila Arsyad Universitas Doktor Nugroho Magetan

DOI:

https://doi.org/10.55382/jurnalpustakarobotsister.v4i2.2253

Kata Kunci:

kecerdasan buatan, klasifikasi, manajemen jaringan, sistem berbasis web, tinjauan literatur sistematis

Abstrak

Penelitian ini bertujuan memetakan perkembangan penerapan Artificial Intelligence (AI) berbasis klasifikasi dalam sistem manajemen jaringan berbasis web serta mengidentifikasi kesenjangan antara pengembangan algoritma dan implementasinya pada sistem operasional. Penelitian menggunakan metode Systematic Literature Review dengan protokol Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Artikel ditelusuri melalui IEEE Xplore, MDPI, SpringerLink, ScienceDirect/Elsevier, Nature/Scientific Reports, Wiley Online Library, Frontiers, dan jurnal nasional yang relevan. Dari 60 artikel kandidat yang diterbitkan pada periode 2021–2025, sebanyak 50 artikel memenuhi kriteria inklusi, eksklusi, dan penilaian kualitas. Data dianalisis berdasarkan tema penelitian, metode klasifikasi, jenis dataset, metrik evaluasi, peran web atau dashboard, serta kesenjangan penelitian. Hasil kajian menunjukkan bahwa penerapan AI didominasi oleh deteksi dan klasifikasi intrusi jaringan sebesar 40%, manajemen jaringan dan klasifikasi lalu lintas sebesar 36%, serta klasifikasi serangan web sebesar 24%. Metode yang digunakan berkembang dari Machine Learning klasik menuju Deep Learning, Ensemble Learning, Federated Learning, dan Hybrid Model. Namun, integrasi model dengan dashboard real-time, data jaringan operasional, interpretabilitas, skalabilitas, dan evaluasi kegunaan masih terbatas. Penelitian ini berkontribusi melalui pemetaan terpadu, identifikasi kesenjangan implementasi, serta usulan kerangka pengembangan sistem manajemen jaringan berbasis web yang akurat, informatif, adaptif, dan mudah digunakan untuk mendukung pengambilan keputusan administrator jaringan secara cepat dan tepat.

Unduhan

Data unduhan belum tersedia.

Referensi

R. H. Serag, M. S. Abdalzaher, H. A. E. A. Elsayed, and M. Sobh, “Software Defined Network Traffic Classification for QoS Optimization Using Machine Learning,” J. Netw. Syst. Manag., vol. 33, no. 2, Apr. 2025, doi: 10.1007/s10922-025-09911-6.

A. A. Ahmed and G. Agunsoye, “A real-time network traffic classifier for online applications using machine learning,” Algorithms, vol. 14, no. 8, Aug. 2021, doi: 10.3390/a14080250.

R. H. Serag, M. S. Abdalzaher, H. A. E. A. Elsayed, M. Sobh, M. Krichen, and M. M. Salim, “Machine-Learning-Based Traffic Classification in Software-Defined Networks,” Electronics (Switzerland), vol. 13, no. 6. Multidisciplinary Digital Publishing Institute (MDPI), Mar. 01, 2024. doi: 10.3390/electronics13061108.

M. L. Ali, K. Thakur, S. Schmeelk, J. Debello, and D. Dragos, “Deep Learning vs. Machine Learning for Intrusion Detection in Computer Networks: A Comparative Study,” Appl. Sci., vol. 15, no. 4, Feb. 2025, doi: 10.3390/app15041903.

Z. Gui et al., “SqliGPT: Evaluating and Utilizing Large Language Models for Automated SQL Injection Black-Box Detection,” Appl. Sci., vol. 14, no. 16, 2024, doi: 10.3390/app14166929.

V. Shanmugam, R. Razavi-Far, and E. Hallaji, “Addressing Class Imbalance in Intrusion Detection: A Comprehensive Evaluation of Machine Learning Approaches,” Electron., vol. 14, no. 1, Jan. 2025, doi: 10.3390/electronics14010069.

H. Park, D. Shin, C. Park, J. Jang, and D. Shin, “Unsupervised Machine Learning Methods for Anomaly Detection in Network Packets,” Electron., vol. 14, no. 14, pp. 1–14, 2025, doi: 10.3390/electronics14142779.

M. Devine, S. P. Ardakani, M. Al-Khafajiy, and Y. James, “Federated Machine Learning to Enable Intrusion Detection Systems in IoT Networks,” Electron., vol. 14, no. 6, 2025, doi: 10.3390/electronics14061176.

F. A. Khan et al., “Balanced Multi-Class Network Intrusion Detection Using Machine Learning,” IEEE Access, vol. 12, pp. 178222–178236, 2024, doi: 10.1109/ACCESS.2024.3503497.

H. M. R. U. Rehman et al., “A systematic literature study of machine learning techniques based intrusion detection: datasets, models, challenges, and future directions,” J. Big Data, vol. 12, no. 1, 2025, doi: 10.1186/s40537-025-01323-2.

M. A. Hossain, “Deep learning-based intrusion detection for IoT networks: a scalable and efficient approach,” Eurasip J. Inf. Secur., vol. 2025, no. 1, 2025, doi: 10.1186/s13635-025-00202-w.

J. Saikam and K. Ch, “EESNN: Hybrid Deep Learning Empowered Spatial-Temporal Features for Network Intrusion Detection System,” IEEE Access, vol. 12, pp. 15930–15945, 2024, doi: 10.1109/ACCESS.2024.3350197.

M. Raza, M. Jasim Saeed, M. B. Riaz, and M. Awais Sattar, “Federated Learning for Privacy-Preserving Intrusion Detection in Software-Defined Networks,” IEEE Access, vol. 12, pp. 69551–69567, 2024, doi: 10.1109/ACCESS.2024.3395997.

N. H. S. Al-Sarray, A. Demirhan, and J. Rahebi, “A robust and scalable intrusion detection framework for SDN with GAN-CL-STO,” J. Supercomput., vol. 81, no. 14, Sep. 2025, doi: 10.1007/s11227-025-07821-7.

S. I. Popoola, Y. Tsado, A. A. Ogunjinmi, E. Sanchez-Velazquez, Y. Peng, and D. B. Rawat, “Multi-Stage Deep Learning for Intrusion Detection in Industrial Internet of Things,” IEEE Access, vol. 13, pp. 60532–60555, 2025, doi: 10.1109/ACCESS.2025.3557959.

M. Cantone, C. Marrocco, and A. Bria, “Machine Learning in Network Intrusion Detection: A Cross-Dataset Generalization Study,” IEEE Access, vol. 12, pp. 144489–144508, 2024, doi: 10.1109/ACCESS.2024.3472907.

E. Casmiry, N. Mduma, and R. Sinde, “Enhanced SQL injection detection using chi-square feature selection and machine learning classifiers,” Front. Big Data, vol. 8, 2025, doi: 10.3389/fdata.2025.1686479.

R. L. Alaoui and E. H. Nfaoui, “Deep Learning for Vulnerability and Attack Detection on Web Applications: A Systematic Literature Review,” Future Internet, vol. 14, no. 4. MDPI, Apr. 01, 2022. doi: 10.3390/fi14040118.

M. Sajid et al., “Enhancing intrusion detection: a hybrid machine and deep learning approach,” J. Cloud Comput., vol. 13, no. 1, Dec. 2024, doi: 10.1186/s13677-024-00685-x.

E. Emirmahmuto?lu and Y. Atay, “A feature selection-driven machine learning framework for anomaly-based intrusion detection systems,” Peer-to-Peer Netw. Appl., vol. 18, no. 3, Jun. 2025, doi: 10.1007/s12083-025-01947-4.

M. Farhan et al., “Network-based intrusion detection using deep learning technique,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-08770-0.

W. Lin and Y. Chen, “Robust Network Traffic Classification Based on Information Bottleneck Neural Network,” IEEE Access, vol. 12, pp. 150169–150179, 2024, doi: 10.1109/ACCESS.2024.3477466.

H. Xue, Y. Hu, and Y. Wang, “Federated Distributed Network Traffic Classification Based on Deep Mutual Learning,” Electron., vol. 14, no. 24, Dec. 2025, doi: 10.3390/electronics14244928.

D. Nuñez-Agurto, W. Fuertes, L. Marrone, E. Benavides-Astudillo, C. Coronel-Guerrero, and F. Perez, “A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking,” Futur. Internet, vol. 16, no. 5, May 2024, doi: 10.3390/fi16050153.

E. Eslami and W. Hamouda, “Network Traffic Classification Using Self-Supervised Learning and Confident Learning,” IEEE Open J. Commun. Soc., vol. 6, pp. 9100–9120, 2025, doi: 10.1109/OJCOMS.2025.3625534.

C. Chindrus and C. F. Caruntu, “Machine Learning-Based Multilabel Classification for Web Application Firewalls: A Comparative Study,” Electron., vol. 14, no. 21, Nov. 2025, doi: 10.3390/electronics14214172.

N. Thalji, A. Raza, M. S. Islam, N. A. Samee, and M. M. Jamjoom, “AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection,” IEEE Access, vol. 11, pp. 135507–135516, 2023, doi: 10.1109/ACCESS.2023.3337645.

X. Wang, J. Zhai, and H. Yang, “Detecting command injection attacks in web applications based on novel deep learning methods,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-74350-3.

J. R. Tadhani, V. Vekariya, V. Sorathiya, S. Alshathri, and W. El-Shafai, “Securing web applications against XSS and SQLi attacks using a novel deep learning approach,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-023-48845-4.

C. M. Rosca, A. Stancu, and C. Popescu, “Machine Learning Models for SQL Injection Detection,” Electron., vol. 14, no. 17, Sep. 2025, doi: 10.3390/electronics14173420.

A. O. Salau and M. M. Beyene, “Software defined networking based network traffic classification using machine learning techniques,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-70983-6.

R. T. Elmaghraby, N. M. Abdel Aziem, M. A. Sobh, and A. M. Bahaa-Eldin, “Encrypted network traffic classification based on machine learning,” Ain Shams Eng. J., vol. 15, no. 2, p. 102361, 2024, doi: 10.1016/j.asej.2023.102361.

Z. Liu, Y. Xie, Y. Luo, Y. Wang, and X. Ji, “TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification,” Appl. Sci., vol. 15, no. 6, 2025, doi: 10.3390/app15062977.

A. Salam, F. Ullah, F. Amin, and A. Mohammad, “Deep Learning Techniques for Web-Based Attack Detection in,” Mdpi, pp. 1–18, 2023.

S. Xu, J. Han, Y. Liu, H. Liu, and Y. Bai, “Few-shot traffic classification based on autoencoder and deep graph convolutional networks,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-94240-6.

E. U. H. Qazi, T. Zia, M. Hamza Faheem, K. Shahzad, M. Imran, and Z. Ahmed, “Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning,” IEEE Access, vol. 12, pp. 141625–141638, 2024, doi: 10.1109/ACCESS.2024.3466470.

L. A. Maghrabi, “Automated Network Intrusion Detection for Internet of Things: Security Enhancements,” IEEE Access, vol. 12, pp. 30839–30851, 2024, doi: 10.1109/ACCESS.2024.3369237.

S. Ness, V. Eswarakrishnan, H. Sridharan, V. Shinde, N. Venkata Prasad Janapareddy, and V. Dhanawat, “Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques,” IEEE Access, vol. 13, pp. 16133–16149, 2025, doi: 10.1109/ACCESS.2025.3526988.

L. Liu, Y. Yu, Y. Wu, Z. Hui, J. Lin, and J. Hu, “Method for multi-task learning fusion network traffic classification to address small sample labels,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-51933-8.

Z. W. Chen, X. X. Wei, and Y. S. Wang, “Encrypted traffic classification encoder based on lightweight graph representation,” Sci. Rep., vol. 15, no. 1, pp. 1–14, 2025, doi: 10.1038/s41598-025-05225-4.

N. Nigar and R. Mustafa, “Enhanced Intrusion Detection via Hybrid Data Resampling and Feature Optimization,” IEEE Access, vol. 13, pp. 149100–149120, 2025, doi: 10.1109/ACCESS.2025.3602562.

M. A. Talukder et al., “Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction,” J. Big Data, vol. 11, no. 1, Dec. 2024, doi: 10.1186/s40537-024-00886-w.

V. Babaey and H. R. Faragardi, “Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders,” Computers, vol. 14, no. 6, Jun. 2025, doi: 10.3390/computers14060205.

Z. Li, F. Liu, Z. Gu, and Y. Liu, “XSS Attack Detection Method Based on CNN-BiLSTM-Attention,” Appl. Sci., vol. 15, no. 16, Aug. 2025, doi: 10.3390/app15168924.

R. Bak?r and H. Bak?r, “Swift Detection of XSS Attacks: Enhancing XSS Attack Detection by Leveraging Hybrid Semantic Embeddings and AI Techniques,” Arab. J. Sci. Eng., vol. 50, no. 2, pp. 1191–1207, Jan. 2025, doi: 10.1007/s13369-024-09140-0.

X. Zhang and J. Yin, “Optimized extreme learning machines with deep learning for high-performance network traffic classification,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-16980-9.

N. Bayat, W. Jackson, and D. Liu, “Deep Learning for Network Traffic Classification,” Jun. 2021, [Online]. Available: http://arxiv.org/abs/2106.12693

E. Tuyishime, M. Martalò, P. A. Cotfas, V. Popescu, D. T. Cotfas, and A. Rekeraho, “Resource-Efficient Traffic Classification Using Feature Selection for Message Queuing Telemetry Transport-Internet of Things Network-Based Security Attacks,” Appl. Sci., vol. 15, no. 8, Apr. 2025, doi: 10.3390/app15084252.

J. Li, Z. Pan, and K. Jiang, “A Three-Dimensional Convolutional Neural Network for Dark Web Traffic Classification Based on Multi-Channel Image Deep Learning,” Computers, vol. 14, no. 8, Aug. 2025, doi: 10.3390/computers14080295.

E. Ganesan, I.-S. Hwang, A. T. Liem, M. Syuhaimi, and A.- Rahman, “photonics hv SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models,” 2021, doi: 10.3390/photonics.