International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Survey of Computer Network Traffic Analysis Using Artificial Intelligence Algorithms

Abstract

This paper introduces a literature review and experimental scenario for network construction; the paper analyzes the network traffic analysis status with artificial intelligence algorithms such as machine learning (ML), ensemble learning, and deep learning for study about analysis in traffic, cybersecurity, balanced loading of network and prediction the traffic moving. The technical aspects of AI are used to analyze and detect attacks or conjunctions for large amounts of network data, thereby detecting anomalies or malicious activities that affect networks. Also, when training deep learning such as convolution neural networks (CNN) or recurrent neural networks (RNN) for datasets (historical data), learn benign network behavior as well as anomalies that may result from malicious activities. The paper introduces how AI technology is used to detect security threats and analyze networks on an outstanding basis.

Country : Iraq

1 Osama Yassin Mohammed2 Ibrahim Ahmed Saleh

  1. Student, Department of Computer Science, College of Computer & Math., University of Mosul, Iraq
  2. Professor, Department of Software, College of Computer & Math., University of Mosul, Iraq

IRJIET, Volume 8, Issue 12, December 2024 pp. 11-19

doi.org/10.47001/IRJIET/2024.812002

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References

  1. M. Abbasi, A. Shahraki, and A. Taherkordi, “Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey,” Comput. Commun., vol. 170, no. September 2020, pp. 19–41, Mar. 2021, doi: 10.1016/j.comcom.2021.01.021.
  2. Y. Yang, “Application of Data Mining Algorithm in Network Security Detections,” Highlights Sci. Eng. Technol., vol. 85, pp. 920–923, 2024, doi: 10.54097/19hgwb09.
  3. H. Huang, S. Chen, R. Ben Basat, H. Dai, A. Taherkordi, and J. Xu, “Guest Editorial Introduction to the Special Section on Next-Generation Traffic Measurement With Network-Wide Perspective and Artificial Intelligence,” IEEE Trans. Netw. Sci. Eng., vol. 11, no. 3, pp. 2332–2337, 2024, doi: 10.1109/TNSE.2024.3389428.
  4. N. Anderson, M. Li, and M. Evans, “Mapping the Ransomware Ecosystem: Tracing Network Traffic to Command & Control Centers.” Sep. 10, 2024. doi: 10.36227/techrxiv.172599578.85351154/v1.
  5. S. Zavrak and M. Iskefiyeli, “Anomaly-Based Intrusion Detection from Network Flow Features Using Variational Autoencoder,” IEEE Access, vol. 8, pp. 108346–108358, 2020, doi: 10.1109/ACCESS.2020.3001350.
  6. B. Wu, J. Xu, Y. Zhang, B. Liu, Y. Gong, and J. Huang, “Integration of computer networks and artificial neural networks for an AI-based network operator,” Appl. Comput. Eng., vol. 64, no. 1, pp. 115–120, 2024, doi: 10.54254/2755-2721/64/20241370.
  7. S. Geißler et al., “Untangling IoT Global Connectivity: The Importance of Mobile Signaling Traffic,” IEEE Trans. Netw. Serv. Manag., vol. 21, no. 4, pp. 4435–4449, Aug. 2024, doi: 10.1109/TNSM.2024.3414975.
  8. S. Ullah Khan, Z. Ulalh Khan, M. Alkhowaiter, J. Khan, and S. Ullah, “Energy-efficient routing protocols for UWSNs: A comprehensive review of taxonomy, challenges, opportunities, future research directions, and machine learning perspectives,” J. King Saud Univ. - Comput. Inf. Sci., vol. 36, no. 7, 2024, doi: 10.1016/j.jksuci.2024.102128.
  9. M. A. M. Farzaan, M. C. Ghanem, A. El-Hajjar, and D. N. Ratnayake, “AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments,” pp. 1–18, 2024, [Online]. Available: http://arxiv.org/abs/2404.05602
  10. F. Genuario, G. Santoro, M. Giliberti, S. Bello, E. Zazzera, and D. Impedovo, “Machine learning-based methodologies for cyber-attacks and network traffic monitoring.” IEEE, Jul. 01, 2024. doi: 10.20944/preprints202407.0029.v1.
  11. S. E. H. Hassan and N. Duong-Trung, “Machine Learning in Cybersecurity: Advanced Detection and Classification Techniques for Network Traffic Environments,” EAI Endorsed Trans. Ind. Networks Intell. Syst., vol. 11, no. 3, pp. 1–22, 2024, doi: 10.4108/eetinis.v11i3.5237.
  12. D. N. Katiyar, M. S. Tripathi, M. P. Kumar, M. S. Verma, D. A. K. Sahu, and D. S. Saxena, “AI and Cyber-Security: Enhancing threat detection and response with machine learning.,” Educ. Adm. Theory Pract., vol. 30, no. 4, pp. 6273–6282, 2024, doi: 10.53555/kuey.v30i4.2377.
  13. A.K. Jakkani, “Real-Time Network Traffic Analysis and Anomaly Detection to Enhance Network Security and Performance: Machine Learning Approaches,” J. Electron. Netw. Appl. Math., no. 44, pp. 32–44, 2024, doi: 10.55529/jecnam.44.32.44.
  14. P. Prakriti, “Cyber Threat Detection Using Machine Learning,” Interantional J. Sci. Res. Eng. Manag., vol. 08, no. 07, pp. 1–15, 2024, doi: 10.55041/ijsrem36799.
  15. I.I. Umukoro, B. O. Eke, and O. Edward, “An efficient intrusion detection technique for traffic pattern learning,” Sci. Africana, vol. 23, no. 2, pp. 25–40, 2024, doi: 10.4314/sa.v23i2.3.
  16. Olakunle Abayomi Ajala and Olusegun Abiodun Balogun, “Leveraging AI/ML for anomaly detection, threat prediction, and automated response,” World J. Adv. Res. Rev., vol. 21, no. 1, pp. 2584–2598, 2024, doi: 10.30574/wjarr.2024.21.1.0287.
  17. S. Wassermann, T. Cuvelier, P. Mulinka, and P. Casas, “Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis,” IEEE Trans. Netw. Serv. Manag., vol. 18, no. 2, pp. 1832–1849, 2021, doi: 10.1109/TNSM.2020.3037486.
  18. J. Mena-Oreja and J. Gozalvez, “A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction,” IEEE Access, vol. 8, pp. 91188–91212, 2020, doi: 10.1109/ACCESS.2020.2994415.

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