Application of Machine Learning in Computer Networks: Techniques, Datasets, and Applications for Performance and Security Optimization
DOI:
https://doi.org/10.35870/ijsecs.v5i1.3989Keywords:
Hybrid Machine Learning, CNN-RNN Framework, Network Security, Threat Detection, Real-Time ProcessingAbstract
This study designs and tests a network security system based on a combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) framework. In this study, distributed processing and reinforcement learning methods in combination with differential privacy are introduced into the proposed system to enhance attack detection and network management. The evaluation results show significant improvements; 97.3% detection accuracy, 34% more efficient bandwidth utilization and 45% less latency than the previous system. The 16-node linear scalability of the distributed architecture has a throughput of 1.2 million packets per second. It is defended against adversarial attacks by maintaining accuracy above 92% and provides a total energy saving of 38% using dynamic batch processing. Three months of testing in an operational environment detected 99.2% of 1,247 threats, including 23 new attack types, with an average detection time of 1.8 seconds. Sensitivity analysis was performed to preserve the privacy of sensitive data while maintaining network performance. The results show that the hybrid solution is reliable, scalable and secure for today's network management.
Downloads
References
Huda, T., & Subektiningsih, S. (2024). Analisis keamanan jaringan komputer menggunakan metode IDS dan IPS dengan notifikasi telegram. Indonesian Journal of Computer Science, 13(1). https://doi.org/10.33022/ijcs.v13i1.3505
Bahri, S. (2023). Perancangan keamanan jaringan untuk mencegah terjadinya serangan bruteforce pada router. Indotech, 1(3), 136-147. https://doi.org/10.60076/indotech.v1i3.239
Kaban, R., Simbolon, M., & Aritonang, R. (2018). Optimasi mikrotik router pada jaringan komputer dan PC-cloning. https://doi.org/10.31227/osf.io/mfk6q
Wijaya, A., & Sutabri, T. (2024). Mendesain cyber security untuk keamanan website menggunakan web application firewall pada kantor BKPSDM Ogan Ilir. Blantika Multidisciplinary Journal, 2(4), 386-395. https://doi.org/10.57096/blantika.v2i4.121
Ardhianto, E., Handoko, W., & Supriyanto, E. (2019). Review perkembangan teknik steganografi dalam lapisan jaringan komputer. Dinamik, 24(1), 6-12. https://doi.org/10.35315/dinamik.v24i1.7837
Amir, J., Mohammad, W., Fauziah, L., Lestari, A., Borut, A., Haryono, B., & lainnya. (2023). Bunga rampai manajemen sumber daya manusia. https://doi.org/10.31219/osf.io/v43p8
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., & Li, Z. (2019). CityFlow: A multi-agent reinforcement learning environment for large-scale city traffic scenario. In Proceedings of the 2019 World Wide Web Conference (pp. 3620-3624). https://doi.org/10.1145/3308558.3314139
Yang, B. (2024). Deep learning-based information security. Applied and Computational Engineering, 97(1), 145-151. https://doi.org/10.54254/2755-2721/97/20241358
Polydoros, A., & Nalpantidis, L. (2017). Survey of model-based reinforcement learning: Applications on robotics. Journal of Intelligent & Robotic Systems, 86(2), 153-173. https://doi.org/10.1007/s10846-017-0468-y
Ghillani, D. (2022). Deep learning and artificial intelligence framework to improve the cyber security. https://doi.org/10.22541/au.166379475.54266021/v1
Zhang, S. (2024). An exploration of the optimization of network security technology based on deep learning algorithms. Advances in Engineering Technology Research, 9(1), 832. https://doi.org/10.56028/aetr.9.1.832.2024
Atadoga, A., Sodiya, E., Umoga, U., & Amoo, O. (2024). A comprehensive review of machine learning's role in enhancing network security and threat detection. World Journal of Advanced Research and Reviews, 21(2), 877-886. https://doi.org/10.30574/wjarr.2024.21.2.0501
Shaukat, K., Luo, S., Varadharajan, V., Hameed, I., & Xu, M. (2020). A survey on machine learning techniques for cyber security in the last decade. IEEE Access, 8, 222310-222354. https://doi.org/10.1109/access.2020.3041951
Hoshino, Y., & Jin'no, K. (2018). Learning algorithm with nonlinear map optimization for neural network. Journal of Signal Processing, 22(4), 153-156. https://doi.org/10.2299/jsp.22.153
Lei, X., Liu, J., & Ye, X. (2024). Research on network traffic anomaly detection technology based on XGBoost (p. 69). https://doi.org/10.1117/12.3051635
Naeem, H. (2023). Analysis of network security in IoT-based cloud computing using machine learning. International Journal for Electronic Crime Investigation, 7(2), Article 153. https://doi.org/10.54692/ijeci.2023.0702153
Jakkani, A. (2024). Real-time network traffic analysis and anomaly detection to enhance network security and performance: Machine learning approaches. Journal of Electronics Computer Networking and Applied Mathematics, 44, 32-44. https://doi.org/10.55529/jecnam.44.32.44
Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020). A review of intrusion detection systems using machine and deep learning in Internet of Things: Challenges, solutions and future directions. Electronics, 9(7), Article 1177. https://doi.org/10.3390/electronics9071177
BaniMustafa, A., Baklizi, M., & Khatatneh, K. (2022). Machine learning for securing traffic in computer networks. International Journal of Advanced Computer Science and Applications, 13(12), Article 252. https://doi.org/10.14569/ijacsa.2022.0131252
Zatadini, T., Wadjdi, A., Wiryana, M., Prakoso, G., Muhammad, F., Zataamani, C., & Rimbawa, H. (2023). Modified of evaluating shallow and deep neural networks for network intrusion detection systems in cyber security. International Journal of Progressive Sciences and Technologies, 42(1), 105. https://doi.org/10.52155/ijpsat.v42.1.5822
Cerri, O., Nguyen, T., Pierini, M., Spiropulu, M., & Vlimant, J. (2019). Variational autoencoders for new physics mining at the large hadron collider. Journal of High Energy Physics, 2019(5), Article 036. https://doi.org/10.1007/jhep05(2019)036
Setiawan, K., & Wibowo, A. (2023). Data mining implementation for detection of anomalies in network traffic packets using outlier detection approach. JIKO (Jurnal Informatika dan Komputer), 6(2), Article 6092. https://doi.org/10.33387/jiko.v6i2.6092
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Memed Saputra, Fegie Yoanti Wattimena, Davy Jonathan
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
