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A Systematic Review for Misuses Attack Detection based on Data Mining in NFV

Year 2023, Volume: 6 Issue: 3, 239 - 252, 31.12.2023

Nebras Ibrahim , Ahmed Abbas , Farah Khorsheed

https://doi.org/10.35377/saucis...1379047

Abstract

Network Function Virtualization could be a quickly advancing innovation that guarantees to revolutionize the way networks are planned, sent, and overseen. However, as with any modern innovation, there are potential security risk that must be tended to guarantee the security of the network. Misuses attacks are one such risk that can compromise the security and integrity of NFV frameworks.
In recently years , data mining has risen as a promising approach for recognizing misuses attacks in NFV systems. This systematic mapping ponders points to supply an overview of the existing research on misuses attack detection based on data mining in NFV. Particularly, the study will recognize and analyze the research conducted in this region, counting the sorts of data mining methods utilized, the types of misuses attacks identified, and the assessment strategies utilized.
The results of this study will give experiences into the current state of investigate on misuses attack detection based on data mining in NFV, as well as recognize gaps and openings for future research in this range. Also, the study will serve as an important asset for analysts and professionals looking for to create successful and effective methods for recognizing misuses attacks in NFV frameworks

Keywords

Misuses attack detection Data mining Network Function Virtualization (NFV) Systematic mapping

References

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  • [30] Cil, A. E., Yildiz, K., & Buldu, A. (2021). Detection of DDoS attacks with feed forward based deep neural network model. Expert Systems with Applications, 169, 114520.

Year 2023, Volume: 6 Issue: 3, 239 - 252, 31.12.2023

Nebras Ibrahim , Ahmed Abbas , Farah Khorsheed

https://doi.org/10.35377/saucis...1379047

Abstract

References

  • [1] Firoozjaei, et al (2017). Security challenges with network functions virtualization. Future Generation Computer Systems, 67, 315-324. ‏ [2] Alnaim, A. K., Alwakeel, A. M., & Fernandez, E. B. (2022). Towards a security reference architecture for NFV. Sensors, 22(10), 3750.
  • [3] Guleria, P., & Sood, M. (2014). Data mining in education: A review on the knowledge discovery perspective. International Journal of Data Mining & Knowledge Management Process, 4(5), 47.‏
  • [4] Saeed, M. M. (2022). A real-time adaptive network intrusion detection for streaming data: a hybrid approach. Neural Computing and Applications, 34(8), 6227-6240.‏
  • [5] Abbas, A. K., Fleh, S. Q., & Safi, H. H. (2015). Systematic Mapping Study On Managing Variability In Software Product Line Engineering: Communication. Diyala Journal of Engineering Sciences, 511-520.
  • [6] Fleh, S. Q., Abbas, A. K., & Saffer, K. M. (2015, December). A systematic mapping study on runtime monitoring of services. In The Iraqi Journal For Mechanical And Material Engineering, Special for Babylon First International Engineering Conference, Issue (A).
  • [7] Hameed, S. S., Hassan, W. H., Latiff, L. A., & Ghabban, F. (2021). A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches. PeerJ Computer Science, 7, e414.‏
  • [8] Zhao, Y., Li, Y., Zhang, X., Geng, G., Zhang, W., & Sun, Y. (2019). A survey of networking applications applying the software defined networking concept based on machine learning. IEEE Access, 7, 95397-95417.‏
  • [9] Ferrag, M. A., Shu, L., Djallel, H., & Choo, K. K. R. (2021). Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0. Electronics, 10(11), 1257.‏
  • [10] Guizani, N., & Ghafoor, A. (2020). A network function virtualization system for detecting malware in large IoT based networks. IEEE Journal on Selected Areas in Communications, 38(6), 1218-1228.‏
  • [11] Sulaiman, N. S., Nasir, A., Othman, W. R. W., Wahab, S. F. A., Aziz, N. S., Yacob, A., & Samsudin, N. (2021, May). Intrusion detection system techniques: a review. In Journal of Physics: Conference Series (Vol. 1874, No. 1, p. 012042). IOP Publishing.
  • [12] Elsevier, https://www.elsevier.com
  • [13] Association for Computing Machinery, https://dl.acm.org/.
  • [14] Proquest, https://www.proquest.com/.
  • [15] IEEE, https://ieeexplore.ieee.org/Xplore/home.jsp.
  • [16] Springer, https://www.springer.com/gp.
  • [17] Lopez-Herrejon, R. E., Linsbauer, L., & Egyed, A. (2015). A systematic mapping study of search-based software engineering for software product lines. Information and software technology, 61, 33-51.‏
  • [18] Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review approach. JBI Evidence Implementation, 13(3), 132-140.‏
  • [19] Shanmugam, B., & Idris, N. B. (2009, December). Improved intrusion detection system using fuzzy logic for detecting anomaly and misuse type of attacks. In 2009 International Conference of Soft Computing and Pattern Recognition (pp. 212-217). IEEE.‏
  • [20] Yan, Q., Yu, F. R., Gong, Q., & Li, J. (2015). Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges. IEEE communications surveys & tutorials, 18(1), 602-622.‏
  • [21] Sharma, P., Johari, R., & Sarma, S. S. (2012). Integrated approach to prevent SQL injection attack and reflected cross site scripting attack. International Journal of System Assurance Engineering and Management, 3, 343-351.‏
  • [22] Kaur, J. (2019). Taxonomy of malware: virus, worms and trojan. Int. J. Res. Anal. Rev, 6(1), 192-196.‏
  • [23] Khan, H. Z. U., & Zahid, H. (2010). Comparative study of authentication techniques. International Journal of Video & Image Processing and Network Security IJVIPNS, 10(04), 09-13.‏
  • [24] Corona, I., Giacinto, G., & Roli, F. (2013). Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues. Information Sciences, 239, 201-225.‏
  • [25] Sharma, H., & Kumar, S. (2016). A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR), 5(4), 2094-2097.‏
  • [26] Stahl, F., & Jordanov, I. (2012). An overview of the use of neural networks for data mining tasks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(3), 193-208.‏
  • [27] Marir, N., Wang, H., Feng, G., Li, B., & Jia, M. (2018). Distributed abnormal behavior detection approach based on deep belief network and ensemble SVM using spark. IEEE Access, 6, 59657-59671.‏
  • [28] Berkhin, P. (2006). A survey of clustering data mining techniques. In Grouping multidimensional data: Recent advances in clustering (pp. 25-71). Berlin, Heidelberg: Springer Berlin Heidelberg.‏
  • [29] Treinen, J. J., & Thurimella, R. (2006). A framework for the application of association rule mining in large intrusion detection infrastructures. In Recent Advances in Intrusion Detection: 9th International Symposium, RAID 2006 Hamburg, Germany, September 20-22, 2006 Proceedings 9 (pp. 1-18). Springer Berlin Heidelberg.‏
  • [30] Cil, A. E., Yildiz, K., & Buldu, A. (2021). Detection of DDoS attacks with feed forward based deep neural network model. Expert Systems with Applications, 169, 114520.
There are 29 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Nebras Ibrahim 0000-0002-8486-5719

Ahmed Abbas 0000-0001-9514-6763

Farah Khorsheed 0009-0007-0087-3038

Early Pub Date December 27, 2023
Publication Date December 31, 2023
Submission Date October 20, 2023
Acceptance Date December 20, 2023
Published in Issue Year 2023Volume: 6 Issue: 3

Cite

IEEE N. Ibrahim, A. Abbas, and F. Khorsheed, “A Systematic Review for Misuses Attack Detection based on Data Mining in NFV”, SAUCIS, vol. 6, no. 3, pp. 239–252, 2023, doi: 10.35377/saucis...1379047.
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