Skip to main navigation menu Skip to main content Skip to site footer
Logo

Articles

Vol. 2 No. 1 (2022): Cybersecurity and Network Defense Research (CNDR)

Intrusion Detection System for Prediction Cyber Threats based on Machine Learning Techniques

Published
15-04-2022

Abstract

The increasing development of cyber threats has urged the establishment of efficient Intrusion Detection Systems that can predict and mitigate these attacks. With the ability to predict future threats and implement mechanisms, machine learning techniques offer excellent solutions in making IDS more intelligent, autonomous, and adaptive for future threats. This paper elaborates the application of multiple ML-based techniques to detect and predict cyber threats within IDS systems, analyzing the effectiveness and performance as well as discussing the limitation areas. Comparisons among supervised, unsupervised, and hybrid models were presented to show each's capability in enhancing accuracy with an IDS and reduction of response times.

References

  1. A. S. Ahanger, S. M. Khan, and F. Masoodi, "An effective intrusion detection system using supervised machine learning techniques," in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021: IEEE, pp. 1639-1644.
  2. Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, "Network intrusion detection system: A systematic study of machine learning and deep learning approaches," Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, p. e4150, 2021.
  3. O. Al-Jarrah, A. Siddiqui, M. Elsalamouny, P. D. Yoo, S. Muhaidat, and K. Kim, "Machine-learning-based feature selection techniques for large-scale network intrusion detection," in 2014 IEEE 34th international conference on distributed computing systems workshops (ICDCSW), 2014: IEEE, pp. 177-181.
  4. M. Al-Omari, M. Rawashdeh, F. Qutaishat, M. Alshira’H, and N. Ababneh, "An intelligent tree-based intrusion detection model for cyber security," Journal of Network and Systems Management, vol. 29, no. 2, p. 20, 2021.
  5. H. Alqahtani, I. H. Sarker, A. Kalim, S. M. Minhaz Hossain, S. Ikhlaq, and S. Hossain, "Cyber intrusion detection using machine learning classification techniques," in Computing Science, Communication and Security: First International Conference, COMS2 2020, Gujarat, India, March 26–27, 2020, Revised Selected Papers 1, 2020: Springer, pp. 121-131.
  6. M. Alrowaily, F. Alenezi, and Z. Lu, "Effectiveness of machine learning based intrusion detection systems," in Security, Privacy, and Anonymity in Computation, Communication, and Storage: 12th International Conference, SpaCCS 2019, Atlanta, GA, USA, July 14–17, 2019, Proceedings 12, 2019: Springer, pp. 277-288.
  7. A. O. Alzahrani and M. J. Alenazi, "Designing a network intrusion detection system based on machine learning for software defined networks," Future Internet, vol. 13, no. 5, p. 111, 2021.
  8. S. V. Amanoul, A. M. Abdulazeez, D. Q. Zeebare, and F. Y. Ahmed, "Intrusion detection systems based on machine learning algorithms," in 2021 IEEE international conference on automatic control & intelligent systems (I2CACIS), 2021: IEEE, pp. 282-287.
  9. U. Aslam, E. Batool, S. N. Ahsan, and A. Sultan, "Hybrid network intrusion detection system using machine learning classification and rule based learning system," International Journal of Grid and Distributed Computing, vol. 10, no. 2, pp. 51-62, 2017.
  10. G. D. C. Bertoli et al., "An end-to-end framework for machine learning-based network intrusion detection system," IEEE Access, vol. 9, pp. 106790-106805, 2021.
  11. K. R. Dalal and M. Rele, "Cyber Security: Threat Detection Model based on Machine learning Algorithm," in 2018 3rd International Conference on Communication and Electronics Systems (ICCES), 2018: IEEE, pp. 239-243.
  12. S. Goel, K. Guleria, and S. N. Panda, "Anomaly based intrusion detection model using supervised machine learning techniques," in 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 2022: IEEE, pp. 1-5.
  13. I. H. Sarker, Y. B. Abushark, F. Alsolami, and A. I. Khan, "Intrudtree: a machine learning based cyber security intrusion detection model," Symmetry, vol. 12, no. 5, p. 754, 2020.
  14. M. A. Hossain and M. S. Islam, "Ensuring network security with a robust intrusion detection system using ensemble-based machine learning," Array, vol. 19, p. 100306, 2023.
  15. M. A. Khan and Y. Kim, "Deep Learning-Based Hybrid Intelligent Intrusion Detection System," Computers, Materials & Continua, vol. 68, no. 1, 2021.
  16. S. Kumar, A. Viinikainen, and T. Hamalainen, "Machine learning classification model for network based intrusion detection system," in 2016 11th international conference for internet technology and secured transactions (ICITST), 2016: IEEE, pp. 242-249.
  17. H. Liu and B. Lang, "Machine learning and deep learning methods for intrusion detection systems: A survey," applied sciences, vol. 9, no. 20, p. 4396, 2019.
  18. U. S. Musa, M. Chhabra, A. Ali, and M. Kaur, "Intrusion detection system using machine learning techniques: A review," in 2020 international conference on smart electronics and communication (ICOSEC), 2020: IEEE, pp. 149-155.
  19. N. Oliveira, I. Praça, E. Maia, and O. Sousa, "Intelligent cyber attack detection and classification for network-based intrusion detection systems," Applied Sciences, vol. 11, no. 4, p. 1674, 2021.
  20. M. Raihan-Al-Masud and H. A. Mustafa, "Network intrusion detection system using voting ensemble machine learning," in 2019 IEEE International Conference on Telecommunications and Photonics (ICTP), 2019: IEEE, pp. 1-4.