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

Early Anomaly Detection in Network Traffic Using Deep Learning Techniques Based on NetFlow Data

Mohammed Abdullah AlrabeeiFaculty of Engineering, Aden University, Aden, YemenMohammed Fadhl AbdullahFaculty of Engineering, Aden University, Aden, Yemen & University of Science and Technology, Aden, Yemen

Vol 10 No 1 (2026): Volume 10, Issue 1, January 2026 | Pages: 185-189

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 04-02-2026

doi Logo doi.org/10.47001/IRJIET/2026.101023

pdf iconFull Text PDF
Abstract

Traditionally, intrusion detection systems have proven unreliable when detecting stealthy or low-rate types of attacks. Increasing numbers of cyber threats have accelerated the need for better ways to monitor network activity. In this paper, we discuss the advantages of analyzing NetFlow data in order to detect intrusion anomalies without packet-level analysis. The methodology we propose is based on deep learning and NetFlow data sets, applied to detect anomalies in a given network environment. We will perform preprocessing on the flow data set and extract the relevant attributes using Autoencoder and LSTM networks. Finally, our findings reveal that this methodology exceeds performance through its enhanced ability to detect subtle attacks. The framework offers a scalable and efficient solution for improving real-time network security.

Keywords

Deep Learning; Network Security, Anomaly Detection, Intrusion Detection, Network Traffic Analysis


Citation of this Article

Mohammed Abdullah Alrabeei,& Mohammed Fadhl Abdullah. (2026). Early Anomaly Detection in Network Traffic Using Deep Learning Techniques Based on NetFlow Data. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(1), 185-189. Article DOI https://doi.org/10.47001/IRJIET/2026.101023

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. CICIDS 2017 Dataset, deep learning. Available: https://www.unb.ca/cic/datasets/ids-2017.html
  2. W. T. Lunardi, M. A. Lopez, J.-P. Giacalone, "ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection," arXiv preprint, 2022.
  3. Y.-C. Wang, Y.-C. Houng, H.-X. Chen, S.-M. Tseng, "Network Anomaly Intrusion Detection Based on Deep Learning Approach," Sensors, vol. 23, no. 4, 2023.
  4. B. J. Radford, L. M. Apolonio, A. J. Trias, J. A. Simpson, "Network Traffic Anomaly Detection Using Recurrent Neural Networks," arXiv preprint, 2018.
  5. "Improved network anomaly detection system using optimized autoencoder-LSTM," Expert Systems with Applications, 2025.
  6. M. Awad, S. Fraihat, K. Salameh, A. Al Redhaei, "Examining the Suitability of NetFlow Features in Detecting IoT Network Intrusions," Sensors, vol. 22, no. 16, pp. 6164, 2022.
  7. "Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues," Knowledge-Based Systems, 2020.
  8. T. Adli, S.-B. Amokrane, B. Pavlović, M. Z. Laidouni, et al., "Anomaly network intrusion detection system based on NetFlow using machine/deep learning," Vojnotehnicki glasnik, vol. 71, no. 4, pp. 941–969, 2023.
  9. A.Miguel-Diez, A. Campazas-Vega, Á. M. Guerrero-Higueras, C. Álvarez-Aparicio and V. Matellán-Olivera, “Anomaly Detection in Network Flows Using Unsupervised Online Machine Learning,” arXiv preprint, 2025. arXiv
  10. E. Roponena, “Anomaly Detection in NetFlow Traffic: Workflow for Dataset and Feature Engineering,” Frontiers in Computer Science, 2025. Frontiers
  11. I.Fosić, “Anomaly Detection in NetFlow Network Traffic Using Machine Learning,” Procedia Computer Sci, 2023.
  12. D. Quirumbay Yagual, D. Fernández Iglesias and F. J. Nóvoa, “A Hybrid Deep Learning-Based Architecture for Network Traffic Anomaly Detection via EFMS-Enhanced KMeans and CNN-GRU Models,” Applied Sciences, vol. 15, no. 20, 10889, 2025. MDPI
  13. T. B. Adli, S.-B. Amokrane, B. Pavlović, M. Z. Laidouni, et al., “Anomaly Network Intrusion Detection System Based on NetFlow Using Machine/Deep Learning,” Vojnotehnički glasnik, vol. 71, no. 4, pp. 941–969, 2023. ResearchGate
  14. A.Koukoulis, I. Syrigos and T. Korakis, “Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems,” arXiv preprint, May 2025. arXiv
  15. E. Caville, W. W. Lo, S. Layeghy and M. Portmann, “Anomal-E: A Self-Supervised Network Intrusion Detection System Based on Graph Neural Networks,” arXiv preprint, Jul. 2022. arXiv
  16. L. Guerra, T. Chapuis, G. Duc, P. Mozharovskyi and V. T. Nguyen, “Self-Supervised Learning of Graph Representations for Network Intrusion Detection,” arXiv preprint, Sep. 2025. arXiv.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.