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

Detection of Cyber Attack in Network Using Machine Learning Networks

K. HarshithaUG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, IndiaS. Ismail sahebAssistant Professor, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.), Andhra Pradesh, IndiaG. BhavanaUG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, IndiaA. AsraarUG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, IndiaC. Irfan bashaUG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, IndiaB. HarishUG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, India

Vol 9 No 3 (2025): Volume 9, Issue 3, March 2025 | Pages: 275-278

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 19-04-2025

doi Logo doi.org/10.47001/IRJIET/2025.903039

pdf iconFull Text PDF
Abstract

Contrasted with the past, improvements in PC and correspondence innovations have given broad and propelled changes. The use of new innovations give incredible advantages to people, organizations, and governments, be that as it may, messes some up against them. For instance, the protection of significant data, security of put away information stages, accessibility of information and so forth. Contingent upon these issues, digital fear based oppression is one of the most significant issues in this day and age. Digital fear, which made a great deal of issues people and establishments, has arrived at a level that could undermine open and nation security by different gatherings, for example, criminal association, proficient people and digital activists. Along these lines, Intrusion Detection Systems (IDS) has been created to maintain a strategic distance from digital assaults. Right now, learning the bolster support vector machine (SVM) calculations were utilized to recognize port sweep endeavors dependent on the new CICIDS2017 dataset with 97.80%, 69.79% precision rates were accomplished individually. Rather than SVM we can introduce some other algorithms like random forest, CNN, ANN where these algorithms can acquire accuracies like SVM – 93.29, CNN – 63.52, Random Forest – 99.93, ANN – 99.11.

Keywords

Cybersecurity, Cyber Attack Detection, Network Security, Machine Learning, Anomaly Detection, Classification Algorithms, Feature Extraction, Network Traffic Analysis, Supervised Learning


Citation of this Article

K. Harshitha, S. Ismail saheb, G. Bhavana, A. Asraar, C. Irfan basha, B. Harish. (2025). Detection of Cyber Attack in Network Using Machine Learning Networks. International Research Journal of Innovations in Engineering and Technology - IRJIET, 9(3), 275-278. Article DOI https://doi.org/10.47001/IRJIET/2025.903039

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. K. Graves, Ceh: Official certified ethical hacker review guide: Exam 312-50. John Wiley & Sons, 2007.
  2. R. Christopher, “Port scanning techniques and the defense against them,” SANS Institute, 2001.
  3. M. Baykara, R. Das¸, and I. Karado ˘gan, “Bilgi g ¨uvenli ˘gisistemlerindekullanilanarac¸larinincelenmesi,” in 1st International Symposium on Digital Forensics and Security (ISDFS13), 2013, pp. 231–239.
  4. S. Staniford, J. A. Hoagland, and J. M. McAlerney, “Practical automated detection of stealthy portscans,” Journal of Computer Security, vol. 10, no. 1-2, pp. 105–136, 2002.
  5. S. Robertson, E. V. Siegel, M. Miller, and S. J. Stolfo, “Surveillance detection in high bandwidth environments,” in DARPA Information Survivability Conference and Exposition, 2003. Proceedings, vol. 1. IEEE, 2003, pp. 130–138.
  6. K. Ibrahimi and M. Ouaddane, “Management of intrusion detection systems based-kdd99: Analysis with lda and pca,” in Wireless Networks and Mobile Communications (WINCOM), 2017 International Conference on. IEEE, 2017, pp. 1–6.
  7. N. Moustafa and J. Slay, “The significant features of the unsw-nb15 and the kdd99 data sets for network intrusion detection systems,” in Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS), 2015 4th International Workshop on. IEEE, 2015, pp. 25–31.
  8. L. Sun, T. Anthony, H. Z. Xia, J. Chen, X. Huang, and Y. Zhang, “Detection and classification of malicious patterns in network traffic using benford’s law,” in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017. IEEE, 2017, pp. 864–872.
  9. S. M. Almansob and S. S. Lomte, “Addressing challenges for intrusion detection system using naive bayes and pca algorithm,” in Convergence in Technology (I2CT), 2017 2nd International Conference for. IEEE, 2017, pp. 565–568.
  10. M. C. Raja and M. M. A. Rabbani, “Combined analysis of support vector machine and principle component analysis for ids,” in IEEE International Conference on Communication and Electronics Systems, 2016, pp. 1–5.
  11. S. Aljawarneh, M. Aldwairi, and M. B. Yassein, “Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model,” Journal of Computational Science, vol. 25, pp. 152–160, 2018.
  12. I.Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization.” in ICISSP, 2018, pp. 108–116.
  13. D. Aksu, S. Ustebay, M. A. Aydin, and T. Atmaca, “Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm,” in International Symposium on Computer and Information Sciences. Springer, 2018, pp. 141–149.
  14. N. Marir, H. Wang, G. Feng, B. Li, and M. Jia, “Distributed abnormal behavior detection approach based on deep belief network and ensemble svm using spark,” IEEE Access, 2018.
  15. P. A. A. Resende and A. C. Drummond, “Adaptive anomaly-based intrusion detection system using genetic algorithm and profiling,” Security and Privacy, vol. 1, no. 4, p. e36, 2018.
  16. C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.
  17. R. Shouval, O. Bondi, H. Mishan, A. Shimoni, R. Unger, and A. Nagler, “Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in sct,” Bone marrow transplantation, vol. 49, no. 3, p. 332, 2014.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.