Identifikasi Malware pada Jaringan Internet sebagai Tindakan Preventif untuk Ancaman Siber berbasis Deep Learning

  • Husnawati Husnawati Politeknik Negeri Sriwijaya ORCID
  • Rian Rahmanda Putra Politeknik Negeri Sriwijaya
  • Faris Humam Politeknik Negeri Sriwijaya
  • Ica Admirani Politeknik Negeri Sriwijaya
  • Reza Maulana Universitas Negeri Sriwijaya
  • M. Humam Fadholi Politeknik Negeri Sriwijaya
  • Abib Ardiansyah Politeknik Negeri Sriwijaya
PDF
DOI https://doi.org/10.31544/jtera.v11.i1.2026.77-86
Published 2026-06-26
Full Text Preview
Download PDF

Abstract

Network security is a crucial aspect for Internet Service Providers (ISPs), especially in the face of the ever-growing threat of malware. Malware is a malicious device designed to attack the operating system or exploit system vulnerabilities. This threat can lead to the theft of critical data and significant losses for users. One of the company networks and internet service providers used by government agencies in South Sumatra faces the challenge of minimizing the risk of malware attacks on the networks it manages. In this study, a deep learning method was applied with the Convolutional Neural Network (CNN) algorithm to identify and classify network traffic indicated by malware proposed. The process was carried out through the stages of collecting network traffic datasets, data pre-processing, training a CNN model, and evaluating model performance. The test results showed that the CNN method was able to detect malware attacks with an accuracy rate of 83%, a precision 83%, and a recall 82%. The application of this method provides a fast and accurate detection system, enabling servers and clients to be more aware of cyber threats and avoid the theft of crucial data.

Keywords

Malware ISP Deep Learning CNN ResNet-18.

References

N. Afifah and D. Stiawan, “The Implementation of Deep Neural Networks Algorithm for Malware Classification,” Comput. Eng. Appl. J., vol. 8, no. 3, pp. 189–202, 2019.
D. Stiawan, S. M. Daely, A. Heryanto, N. Afifah, M. Y. Idris, and R. Budiarto, “Ransomware detection based on opcode behaviour using k-nearest neighbours algorithm,” Inf. Technol. Control, vol. 50, no. 3, pp. 495–506, 2021.
R. B. Hadiprakoso, N. Qomariasih, and R. N. Yasa, “Identifikasi Malware Android Menggunakan Pendekatan Analisis Hibrid Dengan Deep Learning,” J. Teknol. Inf. Univ. Lambung Mangkurat, vol. 6, no. 2, pp. 77–84, 2021.
A. Brown, M. Gupta, and M. Abdelsalam, “Automated machine learning for deep learning based malware detection,” Comput. Secur., vol. 137, pp. 1–17, 2024.
J. Yang, J. Tang, R. Yan, and T. Xiang, “Android Malware Detection Method Based on Permission Complement and API Calls,” Chinese J. …, 2022.
R. Maulana, D. Stiawan, and R. Budiarto, “Detection of android malware with deep learning method using convolutional neural network model,” Comput. Sci. Inf. Technol., vol. 6, no. 1, pp. 68–79, 2025.
D. Stiawan et al., “An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection,” Comput. Syst. Sci. Eng., vol. 46, no. 2, pp. 1759–1774, 2023.
R. Yumlembam, B. Issac, S. M. Jacob, and ..., “Iot-based android malware detection using graph neural network with adversarial defense,” IEEE Internet Things …, 2022.
S. Badhani and S. K. Muttoo, “GENDroid-a graph-based ensemble classifier for detecting Android malware,” Int. J. Inf. …, 2022.
O. Aslan and A. A. Yilmaz, “A New Malware Classification Framework Based on Deep Learning Algorithms,” IEEE Access, vol. 9, pp. 87936–87951, 2021.
S. Hosseini, A. E. Nezhad, and H. Seilani, “Android malware classification using convolutional neural network and LSTM,” J. Comput. Virol. …, 2021.
U. Narayanan, “A novel approach to big data analysis using deep belief network for the detection of android malware,” Indones. J. Electr. Eng. Comput. Sci., vol. 16, no. 3, pp. 1447–1454, 2019.
L. Hübschle-Schneider and P. Sanders, “Parallel Weighted Random Sampling,” ACM Trans. Math. Softw., vol. 48, no. 3, 2022.
N. Xie, X. Di, X. Wang, and J. Zhao, “Andro_MD: Android Malware Detection based on Convolutional Neural Networks,” Int. J. Performability …, 2018.
J. Kim, J. Y. Paik, and E. S. Cho, “Attention-Based Cross-Modal CNN Using Non-Disassembled Files for Malware Classification,” IEEE Access, vol. 11, no. February, pp. 22889–22903, 2023.

Issue

Vol. 11 No. 1: June 2026

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

The copyright to this article is transferred to JTERA if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to JTERA. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form will be sent via email before the article published.

The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.

How to Cite

[1]
“Identifikasi Malware pada Jaringan Internet sebagai Tindakan Preventif untuk Ancaman Siber berbasis Deep Learning”, JTERA, vol. 11, no. 1, pp. 77–86, Jun. 2026, doi: 10.31544/jtera.v11.i1.2026.77-86.