Perbandingan Performa Pembelajaran Mesin untuk Deteksi Ancaman Keamanan Aplikasi Web: Tinjauan Sistematis

Divala Zahra  Oktavia; Dwi Alvin  Hidayat; Desy   Natalia; Satria Krisna  Prabantara; Arief  Arfriandi

doi:10.51903/dhayjg79

Authors

Divala Zahra Oktavia Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia
Dwi Alvin Hidayat Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia
Desy Natalia Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia
Satria Krisna Prabantara Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia
Arief Arfriandi Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia https://orcid.org/0000-0002-5244-8208

DOI:

https://doi.org/10.51903/dhayjg79

Keywords:

Web Application, Threat Detection, Cyber Security, Machine Learning, Systematic Literature Review (SLR)

Abstract

The increasing use of web-based applications across various sectors has led to a growing risk of cyber threats, including phishing, DDoS attacks, SQL Injection, cross-site scripting (XSS), and other forms of intrusion. Traditional detection methods, which are often static, frequently fail to identify newly emerging, dynamic attack patterns. To overcome these limitations, machine learning has emerged as a more adaptive approach that can learn behavioral patterns, detect anomalies, and automatically identify potential threats. This study conducts a Systematic Literature Review (SLR) to investigate the application of machine learning in enhancing web application security and to evaluate the performance of several algorithms commonly used in prior research. The SLR is structured using the PICOC framework, which includes population, intervention, comparison, outcome, and context. The analysis reveals that machine learning techniques are widely applied across various aspects of web security, including phishing detection, malicious URL classification, mitigation of DDoS impacts, and network intrusion detection. Algorithms such as Random Forest, XGBoost, and Support Vector Machine (SVM) consistently demonstrate strong performance, achieving high accuracy across diverse datasets and attack scenarios. Additionally, supporting techniques such as ADASYN, feature selection, and natural language processing-based methods further improve model effectiveness. Overall, the findings of this study highlight that machine learning not only accelerates and improves the precision of threat detection but also adapts to evolving attack patterns, making it a critical component in strengthening the security of modern web applications.

Author Biographies

Divala Zahra Oktavia, Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia

Mahasiswa Departemen Teknik Elektro, Program Studi Teknik Komputer , Fakultas Teknik, Universitas Negeri Semarang
Dwi Alvin Hidayat , Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia

Mahasiswa Departemen Teknik Elektro, Program Studi Teknik Komputer , Fakultas Teknik Universitas Negeri Semarang
Desy Natalia , Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia

Mahasiswa Departemen Teknik Elektro, Program Studi Teknik Komputer , Fakultas Teknik Universitas Negeri Semarang
Satria Krisna Prabantara, Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia

Mahasiswa Departemen Teknik Elektro, Program Studi Teknik Komputer , Fakultas Teknik Universitas Negeri Semarang
Arief Arfriandi, Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang, Kota Semarang, Jawa Tengah, Indonesia

Dosen Departemen Teknik Elektro, Program Studi Teknik Komputer, Fakultas Teknik, Universitas Negeri Semarang

References

[1] D. Mairaj Inamdar and S. Gupta, “A Survey on Web Application Security,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3307, pp. 223–228, 2020, doi: 10.32628/cseit206543.

[2] S. Soni, “Survey on Machine Learning Techniques Used for Information Security,” in Proceedings of the International Conference on Emerging Trends in Artificial Intelligence and Smart Systems (THEETAS 2022), Jabalpur, India, 2022, doi: 10.4108/eai.16-4-2022.2318143.

[3] A. G. Pongoh, R. A. Fahreza, B. Al Kindi, F. S. Pribadi, and R. A. Aprilianto, “Systematic Literature Review (SLR): Dampak Pemanfaatan Artificial Intelligence untuk Meningkatkan Cyber Security,” Cyber Security dan Forensik Digital, vol. 7, no. 1, pp. 34–41, 2024, doi: 10.14421/csecurity.2024.7.1.4486.

[4] 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, p. 118, 2022, doi: 10.3390/fi14040118.

[5] M. Alsaedi, F. A. Ghaleb, F. Saeed, J. Ahmad, and M. Alasli, “Multi-Modal Features Representation-Based Convolutional Neural Network Model for Malicious Website Detection,” IEEE Access, vol. 12, no. December 2023, pp. 7271–7284, 2024, doi: 10.1109/access.2023.3348071.

[6] M. A. B. Dewanto, M. Fathurrahman, D. R. Firdaus, and A. Setiawan, “Penipuan Penambah Followers Instagram: Analisis Serangan Phising dan Dampaknya pada Keamanan Data,” Journal of Internet and Software Engineering, vol. 1, no. 4, p. 11, 2024, doi: 10.47134/pjise.v1i4.2672.

[7] R. S. Wiandani, M. Tahir, I. A. Dyransyha, and R. Ummah, “Identifikasi Serangan SQL Injection Berbantuan Aplikasi Pengujian Keamanan Web DVWA (Damn Vulnerable Web Application),” Digital Transformation Technology, vol. 5, no. 1, pp. 375–382, 2025, doi: 10.47709/digitech.v5i1.5922.

[8] M. Wang, C. Jung, A. Ahad, and Y. Kwon, “Spinner: Automated Dynamic Command Subsystem Perturbation,” Proceedings of the ACM Conference on Computer and Communications Security, pp. 1839–1860, 2021, doi: 10.1145/3460120.3484577.

[9] W. Wahdana and K. H. Hanif, “Implementasi Keamanan Informasi Menggunakan Metode Web Application Firewall terhadap Serangan SQL Injection,” Jurnal Informatika Polinema, vol. 11, no. 4, pp. 399–406, 2025, doi: 10.33795/jip.v11i4.7376.

[10] A. W. Kuncoro and F. Rahma, “Analisis Metode Open Web Application Security Project (OWASP) pada Pengujian Keamanan Website: Literature Review,” Automata, vol. 3, no. 1, pp. 1–5, 2021. [Online]. Available: https://journal.uii.ac.id/automata/article/view/21893

[11] S. Salim and O. Lahcen, “A BERT-Enhanced Exploration of Web and Mobile Request Safety Through Advanced NLP Models and Hybrid Architectures,” IEEE Access, vol. 12, no. April, pp. 76180–76193, 2024, doi: 10.1109/access.2024.3406413.

[12] M. Vebriani and W. Yustanti, “Klasifikasi Deteksi Link Phising DANA Kaget Menggunakan Metode Support Vector Machine Berbasis Website,” Journal of Informatics and Computer Science (JINACS), vol. 6, no. 02, pp. 408–416, 2024, doi: 10.26740/jinacs.v6n02.p408-416.

[13] L. Lukito and W. B. T. Handaya, “Deteksi Website Phishing Menggunakan Teknik Machine Learning,” Jurnal Informatika Atma Jogja, vol. 6, no. 1, pp. 69–80, 2025, doi: 10.24002/jiaj.v6i1.11538.

[14] A. F. Mahmud and S. Wirawan, “Deteksi Phishing Website Menggunakan Machine Learning Metode Klasifikasi,” Sistemasi: Jurnal Sistem Informasi, vol. 13, no. 4, pp. 1368–1380, 2024. [Online]. Available: https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/3456

[15] G. D. Setyawan, A. Yuswanto, A. M. Ridwan, B. Wibowo, and M. Firmansyah, “Implementasi Metode Adasyn dalam Deteksi URL Berbahaya Menggunakan Machine Learning: Demi Meningkatkan Keamanan Siber di Era Digital,” TEKNOKOM: Jurnal Teknologi dan Rekayasa Sistem Komputer, vol. 6, no. 2, pp. 123–126, 2023, doi: 10.31943/teknokom.v6i2.153.

[16] R. Zhao, “Toward the Flow-Centric Detection of Browser Fingerprinting,” Computers & Security, vol. 137, p. 103642, 2024, doi: 10.1016/j.cose.2023.103642.

[17] M. Nawrocki and J. Kołodziej, “Vulnerabilities of Web Applications: Good Practices and New Trends,” Applied Cybersecurity and Internet Governance, vol. 3, no. 2, pp. 122–143, 2024, doi: 10.60097/acig/199521.

[18] 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.

[19] C. J. P. Abuda, “Hybrid Detection Framework Using Natural Language Processing (NLP) and Reinforcement Learning (RL) for Cross-Site Scripting (XSS) Attacks,” International Journal of Advanced Computer Science and Applications, vol. 16, no. 6, pp. 736–747, 2025, doi: 10.14569/ijacsa.2025.0160673.

[20] M. Krishnan, Y. Lim, S. Perumal, and G. Palanisamy, “Detection and Defending the XSS Attack Using Novel Hybrid Stacking Ensemble Learning-Based DNN Approach,” Digital Communications and Networks, vol. 10, no. 3, pp. 716–727, 2024, doi: 10.1016/j.dcan.2022.09.024.

[21] O. Polat, M. Türkoğlu, H. Polat, S. Oyucu, H. Üzen, F. Yardımcı, and A. Aksöz, “Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems,” Sensors, vol. 24, no. 3, p. 1040, 2024, doi: 10.3390/s24031040.

[22] A. Fauzi, E. Utami, and A. D. Hartanto, “DDoS Penerapan Random Forest dan Adaboost untuk Klasifikasi Serangan DDoS,” Journal on Education, vol. 5, no. 3, pp. 7925–7937, 2023, doi: 10.31004/joe.v5i3.1920.

[23] H. Liu and P. Patras, “NetSentry: A Deep Learning Approach to Detecting Incipient Large-Scale Network Attacks,” Comput Commun, vol. 191, pp. 119–132, 2022, doi: 10.1016/j.comcom.2022.04.020.

[24] F. Jemili, K. Jouini, and O. Korbaa, “Detecting Unknown Intrusions from Large Heterogeneous Data Through Ensemble Learning,” Intelligent Systems with Applications, vol. 25, p. 200465, 2025, doi: 10.1016/j.iswa.2024.200465.

[25] M. Lozano, I. Pérez-Llopis, and M. Esteve Domingo, “Threat Hunting Architecture Using a Machine learning Approach for Critical Infrastructures Protection,” Big Data and Cognitive Computing, vol. 7, no. 2, p. 65, 2023, doi: 10.3390/bdcc7020065.

[26] J. Ahmad, S. A. Shah, S. Latif, F. Ahmed, Z. Zou, and N. Pitropakis, “DRaNN_PSO: A Deep Random Neural Network with Particle Swarm Optimization for Intrusion Detection in the Industrial Internet of Things,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 8112–8121, 2022, doi: 10.1016/j.jksuci.2022.07.023.

[27] F. Riza, “Sistem Deteksi Intrusi pada Server secara Realtime Menggunakan Seleksi Fitur dan Firebase Cloud Messaging,” Jurnal Sistim Informasi dan Teknologi, vol. 5, pp. 7–9, 2022, doi: 10.37034/jsisfotek.v5i1.161.

[28] M. K. Baklizi et al., “Web Attack Intrusion Detection System Using Machine Learning Techniques,” International Journal of Online and Biomedical Engineering, vol. 20, no. 3, pp. 24–38, 2024, doi: 10.3991/ijoe.v20i03.45249.

[29] R. Alhamyani and M. Alshammari, “Machine Learning-Driven Detection of Cross-Site Scripting Attacks,” Information (Switzerland), vol. 15, no. 7, p. 420, 2024, doi: 10.3390/info15070420.

[30] S. Kapan and E. Sora Gunal, “Improved Phishing Attack Detection with Machine Learning: A Comprehensive Evaluation of Classifiers and Features,” Applied Sciences (Switzerland), vol. 13, no. 24, p. 13269, 2023, doi: 10.3390/app132413269.

[31] O. Chakir et al., “An Empirical Assessment of Ensemble Methods and Traditional Machine Learning Techniques for Web-Based Attack Detection in Industry 5.0,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 3, pp. 103–119, 2023, doi: 10.1016/j.jksuci.2023.02.009.

[32] A. F. Tjahjono, H. Hasan, R. P. Putera, D. M. P. Indranto, and A. T. Hermawan, “Klasifikasi URL Phishing untuk SIEM: Perbandingan Model Machine Learning XGBoost dan Deep Learning TabNet dalam Deteksi Ancaman Siber,” Sains Data Jurnal Studi Matematika dan Teknologi, vol. 3, no. 2, pp. 62–71, 2025, doi: 10.52620/sainsdata.v3i2.227.

[33] S. Hussain et al., “Optimizing Malicious Website Prediction: An Advanced XGBoost-Based Machine Learning Model,” Nonlinear Engineering, vol. 14, no. 1, p. 20240069, 2025, doi: 10.1515/nleng-2024-0069.

[34] D. Fan, “A Machine Learning Based Cyber Security Threat Prediction Algorithm for Tourism Hotels,” International Journal of Information and Communication Technology, vol. 1, no. 1, pp. 89-103, 2025, doi: 10.1504/ijict.2025.10070773.

[35] J. Wang, “Research on Deep Learning and Feature Aggregation Techniques for Web Security,” Journal of Web Engineering, vol. 24, no. 2, pp. 291–316, 2025, doi: 10.13052/jwe1540-9589.2426.

[36] A. A. R. Farea et al., “Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model,” Computers, Materials and Continua, vol. 76, no. 3, pp. 3605–3662, 2023, doi: 10.32604/cmc.2023.040121.

[37] S. F. Ghazal and S. A. Mjlae, “Cybersecurity in Deep Learning Techniques: Detecting Network Attacks,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 11, pp. 221–230, 2022, doi: 10.14569/ijacsa.2022.0131125.

[38] M. Zaman, D. Upadhyay, and C.-H. Lung, “Validation of a Machine Learning-Based IDS Design Framework Using ORNL Datasets for Power System with SCADA,” IEEE Access, vol. 11, pp. 118414–118426, 2023, doi: 10.1109/access.2023.3326751.

[39] T. Tan, H. Sama, G. Wijaya, and O. E. Aboagye, “Studi Perbandingan Deteksi Intrusi Jaringan Menggunakan Machine Learning: (Metode SVM dan ANN) Comparative Study of Network Intrusion Detection Using Machine learning: (SVM and ANN Method),” Jurnal Teknologi dan Informasi (JATI), vol. 13, no. 2, pp. 152–164, 2023, doi: 10.34010/jati.v13i2.

[40] S. S. Mahmood, “SQL Injection Detection Using Machine Learning and Explainability,” Journal of Internet Services and Information Security, vol. 15, no. 2, pp. 309–324, 2025, doi: 10.58346/jisis.2025.i2.022.

[41] S. Ibarra-Fiallos, J. B. Higuera, M. Intriago-Pazmino, J. R. B. Higuera, J. A. S. Montalvo, and J. Cubo, “Effective Filter for Common Injection Attacks in Online Web Applications,” IEEE Access, vol. 9, pp. 10378–10391, 2021, doi: 10.1109/access.2021.3050566.