Open Access
Issue
ITM Web Conf.
Volume 74, 2025
International Conference on Contemporary Pervasive Computational Intelligence (ICCPCI-2024)
Article Number 02001
Number of page(s) 11
Section Cybersecurity, Networks, and Computing Technologies
DOI https://doi.org/10.1051/itmconf/20257402001
Published online 20 February 2025
  1. D. E. Denning, “An intrusion-detection model,” IEEE Trans. Softw. Eng., vol. SE-13, no. 2, pp. 222–232, Feb. 1987. [CrossRef] [Google Scholar]
  2. N. B. Amor, S. Benferhat, and Z. Elouedi, “Naive Bayes vs decision trees in intrusion detection systems,” in Proc.ACM Symp. Appl. Comput. (SAC), 2004, pp. 420–424. [Google Scholar]
  3. M. Panda and M. R. Patra, “Network intrusion detection using Naive Bayes,” Int. J. Comput. Sci. Netw. Secure., vol. 7, no. 12, pp. 258–263, 2007. [Google Scholar]
  4. M. A. M. Hasan, M. Nasser, B. Pal, and S. Ahmad, “Support vector machine and random forest modeling forintrusion detection system (IDS),’ J. Intell. Learn. Syst. Appl., vol. 6, no. 1, pp. 45–52, 2014. [Google Scholar]
  5. N. Japkowicz, “The class imbalance problem: Significance and strategies,’ in Proc. Int. Conf. Artif. Intell., vol. 56, 2000, pp. 111–117. [Google Scholar]
  6. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,’ Nature, vol. 521, no. 7553, pp. 436–444, 2015. [CrossRef] [Google Scholar]
  7. Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,’ Neurocomputing, vol. 187, pp. 27–48, Apr. 2016. [CrossRef] [Google Scholar]
  8. T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural languageprocessing [review article],’ IEEE Comput. Intell. Mag., vol. 13, no. 3, pp. 55–75, Aug. 2018. [CrossRef] [Google Scholar]
  9. N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,’ IEEETrans. Emerg. Topics Comput. Intell., vol. 2, no. 1, pp. 41–50, Feb. 2018. [CrossRef] [Google Scholar]
  10. D. A. Cieslak, N. V. Chawla, and A. Striegel, “Combating imbalance in network intrusion datasets,’ in Proc.IEEE Int. Conf. Granular Comput., May 2006, pp. 732–737. [Google Scholar]
  11. B. B. Zarpelo, R. S. Miani, C. T. Kawakami, and S. C. de Alvarenga, “A survey of intrusion detection in Internet ofThings,’ J. Netw. Comput. Appl., vol. 84, pp. 2537, Apr. 2017. [Google Scholar]
  12. B. Mukherjee, L. T. Heberlein, and K. N. Levitt, “Network intrusion detection,’ IEEE Network., vol. 8, no. 3, pp. 26–41, May 1994. [CrossRef] [Google Scholar]
  13. S. Kishorwagh, V. K. Pachghare, and S. R. Kolhe, “Survey on intrusion detection system using machine learningtechniques,’ Int. J. Control Automat., vol. 78, no. 16, pp. 30–37, Sep. 2013. [Google Scholar]
  14. N. Sultana, N. Chilamkurti, W. Peng, and R. Alhadad, “Survey on SDN based network intrusion detection systemusing machine learning approaches,’ Peer-to- Peer Netw. Appl., vol. 12, no. 2, pp. 493–501, Mar. 2019. [CrossRef] [Google Scholar]
  15. W. Li, P. Yi, Y. Wu, L. Pan, and J. Li, “A new intrusion detection system based on KNN classification algorithm in wireless sensor network,’ J. Electron. Comput. Eng., vol. 2014, pp. 1–8, Jun. 2014. [Google Scholar]
  16. Subhani Shaik and Dr. Uppu Ravibabu, “Detection and Classification of Power Quality Disturbances Using Curvelet Transform and Support Vector Machines”, in the 5th IEEE International Conference on Information Communication and Embedded System (ICICES-2016) at S.A Engineering College, Chennai, India on 25th - 26th, February 2016. [Google Scholar]
  17. J. Lavanya, M. Ramesh, J. Sravan Kumar, G. Rajaramesh and Subhani Shaik, “Hate Speech Detection Using Decision Tree Algorithm”, Journal of Advances in Mathematics and Computer Science, Volume 38, Issue 8, Page 66–75, June-2023. [CrossRef] [Google Scholar]
  18. Sujan Reddy, Ms. Renu Sri and Subhani Shaik, “Sentimental Analysis using Logistic Regression”, International Journal of Engineering Research and Applications (IJERA), Vol. 11, Series-2, July-2021. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.