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

Machine Learning Model for Denial of Service Network Intrusion Detection

Abstract

Every organization strives to ensure its network is secured from all sorts of attack and is available to its intended users all the time. Network Intrusion Detection Systems (IDS) is one of the techniques used to detect and classify abnormal network access. Therefore, IDS should always be up and up to date with intrusion types and techniques. The most common network intrusion is denial of service (DOS). This study seeks to find out the best machine learning tools that can be used to detect a DOS attack. Using Knowledge Discovery and Data Mining (Knowledge Discovery in Databases) KDD dataset, three machine learning tools are evaluated to find out their performance. The findings show that MLP performs better compared to SVM and KNN.

Country : Kenya

1 Stephen Ngure Gitonga2 Otanga Ananda Daniel3 Stephen Makau Mutua

  1. Information Technology Department, Masinde Muliro University of Science and Technology, Kakamega, Kenya
  2. Information Technology Department, Masinde Muliro University of Science and Technology, Kakamega, Kenya
  3. Computer Science Department, Meru University of Science and Technology, Meru, Kenya

IRJIET, Volume 3, Issue 7, July 2019 pp. 22-28

Full Paper
Download

References

  1. S. Sharma and R. K. Gupta, “Intrusion detection system: A review,” Int. J. Secur. its Appl., 2015.
  2. C. F. Tsai, Y. F. Hsu, C. Y. Lin, and W. Y. Lin, “Intrusion detection by machine learning: A review,” Expert Systems with Applications. 2009.
  3. E. Darra and S. K. Katsikas, “A survey of intrusion detection systems in wireless sensor networks,” in Intrusion Detection and Prevention for Mobile Ecosystems, 2017.
  4. C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel, and M. Rajarajan, “A survey of intrusion detection techniques in Cloud,” Journal of Network and Computer Applications. 2013.
  5. M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Network Anomaly Detection: Methods, Systems and Tools,” IEEE Commun. Surv. {&} Tutorials, vol. PP, pp. 1–34, 2013.
  6. M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Network Anomaly Detection: Methods, Systems and Tools,” IEEE Commun. Surv. Tutorials, 2014.
  7. C. Day, “Intrusion prevention and detection systems,” in Managing Information Security: Second Edition, 2013.
  8. N. Hubballi and V. Suryanarayanan, “False alarm minimization techniques in signature-based intrusion detection systems: A survey,” Computer Communications. 2014.
  9. V. Jyothsna, V. V Rama Prasad, and K. Munivara Prasad, “A Review of Anomaly based Intrusion Detection Systems,” Int. J. Comput. Appl., vol. 28, no. 7, pp. 26–35, 2011.
  10. P. García-Teodoro, J. Díaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges,” Comput. Secur., vol. 28, no. 1–2, pp. 18–28, 2009.
  11. T. Shon and J. Moon, “A hybrid machine learning approach to network anomaly detection,” Inf. Sci. (Ny)., 2007.
  12. M. A. Salama, H. F. Eid, R. A. Ramadan, and A. Darwish, “Hybrid Intelligent Intrusion Detection Scheme,” Springer Berlin Heidelb., 2011.
  13. M. A. Aydin, A. H. Zaim, and K. G. Ceylan, “A hybrid intrusion detection system design for computer network security,” Comput. Electr. Eng., 2009.
  14. G. G. Liu, “Intrusion Detection Systems,” Appl. Mech. Mater., 2014.
  15. E. Hodo et al., “Threat analysis of IoT networks using artificial neural network intrusion detection system,” in 2016 International Symposium on Networks, Computers and Communications, ISNCC 2016, 2016.
  16. M. Govindarajan and R. Chandrasekaran, “Intrusion detection using neural based hybrid classification methods,” Comput. Networks, 2011.
  17. A.Tajbakhsh, M. Rahmati, and A. Mirzaei, “Intrusion detection using fuzzy association rules,” Appl. Soft Comput. J., 2009.
  18. G. P. Rout and S. N. Mohanty, “A hybrid approach for network intrusion detection,” in Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015, 2015.
  19. M. Ahmed, A. Naser Mahmood, and J. Hu, “A survey of network anomaly detection techniques,” Journal of Network and Computer Applications. 2016.
  20. M. Teng, “Anomaly detection on time series,” in Proceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing, PIC 2010, 2010.
  21. J. Jabez and B. Muthukumar, “Intrusion detection system (ids): Anomaly detection using outlier detection approach,” in Procedia Computer Science, 2015.
  22. D. E. Denning, “An intrusion-detection model,” in Proceedings - IEEE Symposium on Security and Privacy, 2012.
  23. W. Lee and S. J. Stolfo, “A framework for constructing features and models for intrusion detection systems,” ACM Trans. Inf. Syst. Secur., 2000.
  24. T. Oladipupo, “Types of Machine Learning Algorithms,” in New Advances in Machine Learning, 2010.
  25. A.Dey and A. S. Learning, “Machine Learning Algorithms : A Review,” Int. J. Comput. Sci. Inf. Technol., 2016.
  26. N. Gupta, “Artificial Neural Network,” Netw. Complex Syst., 2013.
  27. K. L. Du and M. N. S. Swamy, Neural networks and statistical learning. 2014.
  28. G. M. Khan, “Artificial neural network (ANNs),” in Studies in Computational Intelligence, 2018.
  29. W. L. Al-Yaseen, Z. A. Othman, and M. Z. A. Nazri, “Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system,” Expert Syst. Appl., 2017.
  30. Y. Ma and G. Guo, Support vector machines applications. 2014.
  31. B. Schölkopf, “An Introduction to Support Vector Machines,” in Recent Advances and Trends in Nonparametric Statistics, 2003.
  32. Bhargava and Sharma, “Decision Tree Analysis on J48 Algorithm for Data Mining,” Int. J. Adv. Res. Decis. Tree Anal. J48 Algorithm Data Min., 2013.
  33. S. Drazin and M. Montag, “Decision Tree Analysis using Weka,” 2010.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies