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All issues Volume 430 (2023) E3S Web Conf., 430 (2023) 01064 References
Open Access
Issue
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
Article Number 01064
Number of page(s) 13
DOI https://doi.org/10.1051/e3sconf/202343001064
Published online 06 October 2023
  1. Venkata Ramana, Chandra Mouli, Aileni Eenaja “Network Intrusion Detection By SVM & ANN With Feature Selection” (2020) IJCRT | Volume 8, Issue 6 June 2020 | ISSN: 2320-2882. [Google Scholar]
  2. Intrusion Detection Systems Part I – (network intrusions; attack symptoms; IDS tasks; and IDS architecture) By Przemyslaw Kazienko Piotr Dorosz / April 7, (2003) [Google Scholar]
  3. H. Song, M. J. Lynch, and J. K. Cochran, “A macro-social exploratory analysis of the rate of interstate cyber-victimization,” American Journal of Criminal Justice, vol. 41, no. 3, pp. 583–601, (2016). [CrossRef] [Google Scholar]
  4. P. Alaei and F. Noorbehbahani, “Incremental anomaly-based intrusion detection system using limited labeled data,” in Web Research (ICWR), 2017 3th International Conference on, 2017, pp. 178–184. [Google Scholar]
  5. M. Saber, S. Chadli, M. Emharraf, and I. El Farissi, “Modeling and implementation approach to evaluate the intrusion detection system,” in International Conference on Networked Systems, (2015), pp. 513–517. [Google Scholar]
  6. M. Tavallaee, N. Stakhanova, and A. A. Ghorbani, “Toward credible evaluation of anomaly-based intrusion-detection methods,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 5, pp. 516–524, (2010). [CrossRef] [Google Scholar]
  7. A. S. Ashoor and S. Gore, “Importance of intrusion detection system (IDS),” International Journal of Scientific and Engineering Research, vol. 2, no. 1, pp. 1–4, (2011). [Google Scholar]
  8. M. Zamani and M. Movahedi,arXivpreprintarXiv:1312.2177, (2013). DOI: https://doi.org/10.48550/arXiv.1312.2177 [Google Scholar]
  9. N. Chakraborty, “Intrusion detection system and intrusion prevention system: A comparative study,” IJCBR ISSN (Online), Volume 4 Issue 2 May (2013) pp. 2229–6166, DOI: https://link.springer.com/chapter/10.1007/978-981-16-8012-0_4. [Google Scholar]
  10. P. Garcia-Teodoro, J. Diaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, Computers & Security, vol.28, no. 1–2, pp. 18–28,(2009). DOI: https://doi.org/10.1016/j.cose.2008.08.003 [CrossRef] [Google Scholar]
  11. M. C. Belavagi and B. Muniyal, Procedia Computer Science, vol. 89, pp. 117–123, (2016). DOI: https://doi.org/10.1016/j.procs.2016.06.016 [CrossRef] [Google Scholar]
  12. J. Zheng, F. Shen, H. Fan, and J. Zhao, Neural Computing and Applications, vol. 22, no. 5, pp. 1023–1035, (2013). [CrossRef] [Google Scholar]
  13. F. Gharibian and A. A. Ghorbani, “Comparative study of supervised machine learning technique” In proceedings of Fifth Annual Conference on Communication Networks and Services Research (CNSR '07) Year:(2007), Pages: 350-358 DOI: 10.1109/CNSR.2007.22 . [Google Scholar]
  14. A Khraisat, I Gondal, P Vamplew, J Kamruzzaman. Khraisat et al. Cybersecurity. (2019) pages: 2:20. DOI: https://doi.org/10.1186/s42400-019-0038-7 . [CrossRef] [Google Scholar]
  15. J Foley, N Moradpoor, H Ochenyi. Hindawi Security and Communication Networks (2020), Article ID 2804291, 17 pages DOI: https://doi.org/10.1155/2020/2804291 [Google Scholar]
  16. P. Rajesh Kanna, P. Santhi. Expert Systems with Applications. Volume 194, 116545(2022). DOI: https://doi.org/10.1016/j.eswa.2022.116545 . [CrossRef] [Google Scholar]
  17. Vijayakumar, Alazab, Soman, Poornachandran, Al-Nemrat, S. “A Deep Learning Approach for Intelligent Intrusion Detection System”. IEEE Access (2019). DOI: https://doi.org/10.1109/ACCESS.2019.2895334. [Google Scholar]
  18. Ch. V. Raghavendran, G. Naga Satish, Rama Reddy T., B. Annapurna. “Building Time Series Prognostic Models to Analyze the Spread of COVID-19 Pandemic”. IJAST, Vol.29 No.3 (2020),13258. DOI: http://sersc.org/journals/index.php/IJAST/article/view/31524 [Google Scholar]
  19. Raghavendran C.V., Pavan Venkata Vamsi C., Veerraju T., Veluri R.K. (2021) Advances in Intelligent Systems and Computing, vol 1280. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-15-9516-5_13 [Google Scholar]
  20. K. Prathyusha, K. Helini, C. V. Raghavendran and N. Kumar Kurumeti, “COVID-19 in India: Lockdown analysis and future predictions using Regression models,” 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India (2021), pp. 899-904, DOI: 10.1109/Confluence51648.2021.9377052. [Google Scholar]
  21. G. Naga Satish, Ch.V. Raghavendran, R.S. Murali Nath (2021). In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-33-6081-5_7 [Google Scholar]
  22. Ch Mallikarjuna Rao et al., IJRTE, ISSN: 2277-3878, Volume-8, Issue-1, May (2019). [Google Scholar]
  23. Karanam Madhavi, et.al, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, 8 (1) May (2019). [Google Scholar]
  24. K.Madhavi et al,” Routing In Wireless Sensor Networks Using Machine Learning Techniques : Challenges and Opportunities.” Measurements January 2021. SCI. SCOPUS Indexed. https://doi.org/10.1016/j.measurement.2021.108974 [Google Scholar]
  25. Dhanke Jyoti Atul, R. Kamalraj, G. Ramesh, K. Sakthidasan Sankaran, Sudhir Sharma, Syed Khasim et al. (2021). Microprocessors and Microsystems, Volume 82, 103741. DOI: https://doi.org/10.1016/j.micpro.2020.103741 [CrossRef] [Google Scholar]
  26. Shalli Rani, M. Balasaraswathi, P. Chandra Sekhar Reddy, Gurbinder Singh Brar, M. Sivaram & Vigneswaran Dhasarathan, Wireless Networks, 26, (2020) [Google Scholar]
  27. Chandrika Lingala, and Karanam Madhavi et.al, “A Survey on Cardivascular Prediction using Variant Machine learning Solutions.” E3S Web of Conferences 309, 01042 (2021), ICMED 2021. https://doi.org/10.1051/e3sconf/202130901042 [Google Scholar]
  28. Latha, S.B., Gundavarapu, M.R., Kumar, N.V.S.P., Parameswari, D.V.L., Reddy, B.R.K., (IJRITCC) 11(3), 273–282(2023) [CrossRef] [Google Scholar]
  29. “Data Science: Identifying Influencers in Social Networks” in Periodicals of Engineering and Natural Sciences Vol.6, Issue.1, June 2018 PP 215-228. [Google Scholar]
  30. Pradeep, G., Sakthidharan, G.R.”A Survey on Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection”, International Conference on Machine Learning Big Data Management Cloud and Computing, (2021) [Google Scholar]

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