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Abstract

The open source RRDtool software solves the challenge of collecting and storing time series data from service networks. However, even an experienced network technician will find it difficult to monitor all relevant service network time series at the same time. The approach is to incorporate a mathematical model into the monitoring application that can automatically detect deviations in the time series. And when we talk about the growing number of models, the choice is difficult, because the chosen model must be compatible with real-time monitoring, in this paper we adopt a new approach which is to integrate the theory of exponential smoothing and intelligent properties in Holt Winters algorithm by using RRDtool as a tool for data collection and representation With real-time schemes, although this technique is not perfect, it is adaptable, effective and successful as a tool for automatic identification of deviant behavior after being trained to identify anomalies through prediction based on past and future steps.

Keywords

RRDtool Exponential Smoothing Holt-Winters Forecasting Algorithm timestamps

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Creative Commons License

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

How to Cite
Alaa Abdulhussein Daleh Al-magsoosi. (2022). Methods for network monitoring to detect anomalies in time series. Texas Journal of Engineering and Technology, 8, 84–93. Retrieved from https://zienjournals.com/index.php/tjet/article/view/1675

References

  1. J. R. Langevin, M. T. McCaul, S. Charney, and H. Raduege, "Securing cyberspace for the 44th presidency," CENTER FOR STRATEGIC AND INTERNATIONAL STUDIES WASHINGTON DC2008.
  2. R. J. R. Langevin, R. M. T. McCaul, S. Charney, L. G. H. Raduege, and J. A. Lewis, "Cybersecurity two years later," Washington, DC: Center for Strategic and International Studies, 2011.
  3. R. M. Al_airaji, H. Salim, and A. M. Alaidi, "Automated Cheating Detection based on Video Surveillance in the Examination Classes," iJIM, vol. 16, no. 10, 2022.
  4. B. Augustin and A. Mellouk, "On traffic patterns of http applications," in 2011 IEEE Global Telecommunications Conference-GLOBECOM 2011, 2011, pp. 1-6: IEEE.
  5. H. T. ALRikabi and H. T. Hazim, "Enhanced Data Security of Communication System Using Combined Encryption and Steganography," International Journal of Interactive Mobile Technologies, vol. 15, no. 16, 2021.
  6. A. Edition, "Information Operations Primer," 2006.
  7. W. Jiang, X. Wu, Y. Gong, W. Yu, and X. Zhong, "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, vol. 193, p. 116779, 2020.
  8. E. M. de Oliveira and F. L. C. Oliveira, "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, vol. 144, pp. 776-788, 2018.
  9. I. A. Aljazaery, H. T. Salim ALRikabi, and A. H. M. Alaidi, "Encryption of Color Image Based on DNA Strand and Exponential Factor," International Journal of Online Biomedical Engineering, vol. 18, no. 3, pp. 101-113, 2022.
  10. C. Liu, B. Sun, C. Zhang, and F. Li, "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied energy, vol. 275, p. 115383, 2020.
  11. Y. Zhu, Y. Zhao, J. Zhang, N. Geng, and D. Huang, "Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model," PloS one, vol. 14, no. 7, p. e0219889, 2019.
  12. I. A. A. Haider TH. Salim ALRikabi, Jaafar Sadiq Qateef, Abdul Hadi M. Alaidi, Roa'a M. Al_airaji, "Face Patterns Analysis and recognition System based on Quantum Neural Network QNN," International Journal of Interactive Mobile Technologies (iJIM), vol. 16, no. 9, 2022.
  13. D. Bissing, M. T. Klein, R. A. Chinnathambi, D. F. Selvaraj, and P. Ranganathan, "A hybrid regression model for day-ahead energy price forecasting," Ieee Access, vol. 7, pp. 36833-36842, 2019.
  14. J. Zhang, C. Wu, Y. Wang, and P. Wang, "Detection of abnormal behavior in narrow scene with perspective distortion," Machine Vision and Applications, vol. 30, no. 5, pp. 987-998, 2019.
  15. P. K. Dunstan, S. D. Foster, E. King, J. Risbey, T. J. O’Kane, D. Monselesan, A. J. Hobday, J. R. Hartog, and P. A. Thompson, "Global patterns of change and variation in sea surface temperature and chlorophyll a," Scientific reports, vol. 8, no. 1, pp. 1-9, 2018.
  16. A. F. Al-zubidi, N. F. AL-Bakri, R. K. Hasoun, and S. H. Hashim, "Mobile Application to Detect Covid-19 Pandemic by Using Classification Techniques: Proposed System," International Journal of Interactive Mobile Technologies, vol. 15, no. 16, 2021.
  17. C. Dupke, C. Bonenfant, B. Reineking, R. Hable, T. Zeppenfeld, M. Ewald, and M. Heurich, "Habitat selection by a large herbivore at multiple spatial and temporal scales is primarily governed by food resources," Ecography, vol. 40, no. 8, pp. 1014-1027, 2017.
  18. S. H. Abbood, H. N. Abdull Hamed, M. S. Mohd Rahim, A. H. M. Alaidi, and H. T. Salim ALRikabi, "DR-LL Gan: Diabetic Retinopathy Lesions Synthesis using Generative Adversarial Network," International Journal of Online and Biomedical Engineering(iJOE), vol. 18, no. 3, 2022.
  19. A. Sahai, "VM Power Prediction in Distributed Systems for Maximizing Renewable Energy Usage," arXiv preprint arXiv:1402.5642, 2014.
  20. J. D. Brutlag, "Aberrant behavior detection in time series for network service monitoring," in 14th Systems Administration Conference (LISA 2000), 2000.
  21. N. Alseelawi, and H. T. Hazim, "A Novel Method of Multimodal Medical Image Fusion Based on Hybrid Approach of NSCT and DTCWT," International Journal of Online Biomedical Engineering, vol. 18, no. 3, 2022.
  22. T. Öhman, L. Gawriyski, S. Miettinen, M. Varjosalo, and S. Loukovaara, "Molecular pathogenesis of rhegmatogenous retinal detachment," Scientific reports, vol. 11, no. 1, pp. 1-15, 2021.
  23. M. K. Abdul-Hussein and H. Alrikabi, "Evaluation of the Interference’s Impact of Cooperative Surveillance Systems Signals Processing for Healthcare," International Journal of Online and Biomedical Engineering(iJOE), vol. 18, no. 3, pp. 43-59, 2022.
  24. D. Zhang, N. Jayasena, A. Lyashevsky, J. L. Greathouse, L. Xu, and M. Ignatowski, "TOP-PIM: Throughput-oriented programmable processing in memory," in Proceedings of the 23rd international symposium on High-performance parallel and distributed computing, 2014, pp. 85-98.
  25. S. T. Roberts, "Behind the screen," in Behind the Screen: Yale University Press, 2019.
  26. S. Bouktif, A. Fiaz, A. Ouni, and M. A. Serhani, "Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches," Energies, vol. 11, no. 7, p. 1636, 2018.
  27. H. Qiu, T. Dong, T. Zhang, J. Lu, G. Memmi, and M. Qiu, "Adversarial attacks against network intrusion detection in iot systems," IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10327-10335, 2020.
  28. X. Tang, T. Qian, and Z. You, "Generating behavior features for cold-start spam review detection with adversarial learning," Information Sciences, vol. 526, pp. 274-288, 2020.
  29. P. Rodríguez, M. A. Bautista, J. Gonzàlez, and S. Escalera, "Beyond one-hot encoding: Lower dimensional target embedding," Image and Vision Computing, vol. 75, pp. 21-31, 2018.