Skip to main content
SpringerLink
Log in

Application of Machine Learning Techniques in Intrusion Detection Systems: A Systematic Review

  • Conference paper
  • First Online:
Proceedings of Third International Conference on Sustainable Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1404))

Abstract

In recent years, the developments in the domains of technology, communication and Internet have led to a drastic increase in cybercrimes, hacking, and other online frauds, as unauthorized users try to breach the security policies and gain access to resources falsely. This is due to the fact that we are using Computers and Internet in almost all aspects of our life like Shopping, Banking, etc. Security is an important feature for almost all the systems in this real world and at the current time, it is necessary to keep our systems safe from such security breaches. Intrusion Detection System (IDS) is an important tool or solution that can be implemented and deployed on networks or systems or both to keep them secure and away from unauthorized access. It monitors the network or system and looks for an abnormal activity; in such a case, it generates an alarm signifying that some intrusion or malicious event has occurred in the system. Machine Learning (ML) plays an important role in enhancing the performance of a system by making it intelligent. ML-based approaches will ensure that IDS will acquire new knowledge while operating based on existing knowledge and will be able to detect new or unknown attacks with ease. This paper provides a brief introduction about the IDS, ML-based approaches, recent works being carried out by other researchers for implementing the ML-based IDS models, and a comparative analysis of all those works specifying the benefits and shortcomings of each of them.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. S.S. Roy, A. Malik, R. Gulati, M.S. Obaidat, P.V. Krishna, A deep learning based artificial neural network approach for intrusion detection, in Proceedings of International Conference on Mathematics and Computing (ICMC-2017) (Springer, 2017), pp 44–53

    Google Scholar 

  2. S. Kumar, A. Viinikainen, T. Hamalainen, Machine learning classification model for network based intrusion detection system, in Proceedings of the 11th International Conference for Internet Technology and Secured Transactions (ICITST-2016) (IEEE, 2016), pp. 242–249

    Google Scholar 

  3. G. Karatas, O. Demir, O.K. Sahingoz, Deep learning in intrusion detection systems, in Proceedings of International Conference on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT-2018) (IEEE, 2018), pp. 113–116

    Google Scholar 

  4. E.K. Veigas, A.O. Santin, L.S. Oliveira, Toward a reliable anomaly based intrusion detection in real world environments. J. Comput. Netw. 127, 200–216 (2017)

    Google Scholar 

  5. W. Stallings, Cryptography and Network Security: Principles and Practice, 5th edn. (Prentice Hall (Pearson) Publications, 2010)

    Google Scholar 

  6. A. Kahate, Cryptography and Network Security, 4th edn. (Tata McGraw Hill Publications, 2019)

    Google Scholar 

  7. T. Mehmood, H.B.M. Rais, Machine learning algorithms in context of intrusion detection, in Proceedings of 3rd International Conference on Computer and Information Sciences (ICCOINS) (IEEE, 2016), pp. 369–373

    Google Scholar 

  8. K. Kim, M.E. Aminanto, Deep learning in intrusion detection perspective: overview and further challenges, in Proceedings of International Workshop on Big Data and Information Security (IEEE, 2017), pp. 5–10

    Google Scholar 

  9. M. Almseidin, M. Alzubi, S. Kovacs, M. Alkasassbeh, Evaluation of machine learning algorithms for intrusion detection system, in Proceedings of 15th International Symposium on Intelligent Systems and Informatics (IEEE, 2017), pp. 277–282

    Google Scholar 

  10. N.T. Van, T.N. Thinh, L.T. Sach, An anomaly based network intrusion detection system using deep learning, in Proceedings of International Conference on System Science and Engineering (ICSSE) (IEEE, 2017), pp. 210–214

    Google Scholar 

  11. R.K. Sharma, H.K. Kalita, P. Borah, Analysis of machine learning techniques based intrusion detection systems, in Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics (Springer, 2016), pp. 485–493

    Google Scholar 

  12. R. Makani, B.V.R. Reddy, Taxonomy of machine learning based anomaly detection and its suitability, in Proceedings of International Conference on Computational Intelligence and Data Science (ICCIDS 2018) published under Procedia Computer Science, vol. 132 (Elsevier, 2018), pp. 1842–1849

    Google Scholar 

  13. A.A. Shah, M.S.H. Khiyal, M.D. Awan, Analysis of machine learning techniques for intrusion detection system: a systematic review. Int. J. Comput. Appl. 119(3), 19–29 (2015)

    Google Scholar 

  14. D. Kwon, H. Kim, J. Kim, S.C. Suh, I. Kim, K.J. Kim, A survey of deep learning based network anomaly detection. J. Cluster Comput. 22, 949–961 (2017)

    Google Scholar 

  15. H. Kour, N. Gondhi, Machine learning techniques: a survey, in Proceedings of International Conference on Innovative Data Communication Technologies and Applications (ICIDCA), published under Lecture Notes on Data Engineering and Communications Technologies (LNDECT), vol. 46 (Springer, 2020), pp. 266–275

    Google Scholar 

  16. A. Chawla, B. Lee, S. Fallon, P. Jacob, Host based intrusion detection system with combined CNN/RNN model, in Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2018), published under Lecture Notes in Computer Science (LNCS), vol. 11329 (Springer, 2019), pp. 149–158

    Google Scholar 

  17. Divyatmika, M. Sreekesh, A two-tier network based intrusion detection system architecture using machine learning approach, in Proceedings of International Conference on Electrical, Electronics and Optimization Techniques (ICEEOT 2016) (IEEE, 2016), pp. 42–47

    Google Scholar 

  18. P. Tao, Z. Sun, Z. Sun, An improved intrusion detection algorithm based on GA and SVM. Published in IEEE ACCESS under Special Section on Human-Centered Smart Systems and Technologies, vol. 6 (IEEE, 2018), pp. 13624–13631

    Google Scholar 

  19. D. Narsingyani, O. Kale, Optimizing false positive in anomaly based intrusion detection using genetic algorithm, in Proceedings of 3rd International Conference on MITIE (IEEE, 2015), pp. 72–77

    Google Scholar 

  20. R. Vinayakumar, A. Mamoun, K.P. Soman, P. Prabaharan, A.N. Ameer, V. Sitalakshmi, Deep learning approach for intelligent intrusion detection system. IEEE Access 7, 41525–41550 (2019)

    Google Scholar 

  21. G. Karatas, O.K. Sahingoz, Neural network based intrusion detection systems with different training functions, in Proceedings of 6th International Symposium on Digital Forensic and Security (ISDFS) (IEEE, 2018)

    Google Scholar 

  22. C. Yin, Y. Zhu, J. Fei, X. He, A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017)

    Google Scholar 

  23. X. Gao, C. Shan, C. Hu, Z. Niu, Z. Liu, An adaptive ensemble machine learning model for intrusion detection. Published in IEEE Access under Special Session on Artificial Intelligence in Cyber-Security, vol. 7 (IEEE, 2019), pp. 82512–82521

    Google Scholar 

  24. H.M. Tahir, W. Hasan, A.M. Said, N.H. Zakaria, N. Kutak, N.F. Kabir, M.H. Omar, O. Ghazali, N.I. Yahya, Hybrid machine learning technique for intrusion detection system, in Proceedings of the 5th International Conference on Computing and Informatics (ICOCI 2015), pp. 464–472

    Google Scholar 

  25. H. Zhang, C.Q. Wu, S. Gao, Z. Wang, Y. Xu, Y. Liu, An effective deep learning based scheme for network intrusion detection, in Proceedings of the 24th International Conference on Pattern Recognition (ICPR) (IEEE, 2018), pp. 682–687

    Google Scholar 

  26. A. Meryem, B.E. Ouahidi, Hybrid intrusion detection system using machine learning. J. Netw. Secur. 2020(5), 8–19 (2020)

    Google Scholar 

  27. B. Ingre, A. Yadav, A.K. Soni, Decision tree based intrusion detection system for NSL-KDD dataset, in Proceedings of International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2017), published under Smart Innovation, Systems and Technologies (SIST), vol. 2 (Springer, 2017), pp. 207–218

    Google Scholar 

  28. S. Kumar, B. Sharma, V.K. Sharma, R.C. Poonia, Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm. Evol. Intel. 1–12 (2018). https://doi.org/10.1007/s12065-018-0186-9

  29. S. Kumar, B. Sharma, V.K. Sharma, H. Sharma, J.C. Bansal, Plant leaf disease identification using exponential spider monkey optimization. Sustainable Comput.: Inf. Syst. 28 (2018). https://doi.org/10.1016/j.suscom.2018.10.004

  30. M. Ring, S. Wunderlich, D. Scheuring, D. Landes, A. Hotho, A survey of network-based intrusion detection data sets. J. Comput. Secur. 86, 147–167 (2019)

    Google Scholar 

  31. P. Mishra, V. Vardharajan, U. Tupakula and E. S. Pilli, A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun.: Surv. Tutorials 21(1), 686–728 (2018)

    Google Scholar 

  32. K. Yang, J. Liu, C. Zhang, Y. Fang, Adversarial examples against the deep learning based network intrusion detection system, in Proceedings of IEEE Military Communications Conference (MILCOM) (IEEE, 2018), pp. 559–564

    Google Scholar 

  33. C.H. Lee, Y.Y. Su, Y.C. Lin, S.J. Lee, Machine learning based network intrusion detection, in Proceedings of 2nd IEEE International Conference on Computational Intelligence and Applications (IEEE, 2017), pp. 79–83

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, TIEIT, Bhopal, M.P., India

    Puneet Himthani

  2. Department of Computer Science and Engineering, SISTec, Bhopal, M.P., India

    Ghanshyam Prasad Dubey

Authors
  1. Puneet Himthani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghanshyam Prasad Dubey
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, CHRIST (Deemed to be University), Bengalore, Karnataka, India

    Ramesh Chandra Poonia

  2. Department of Computer Science and Engineering, Manipal University, Jaipur, Rajasthan, India

    Vijander Singh

  3. Faculty of Computing and Informatics, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  4. Data Science Research Group, National University of La Pampa, Santa Rosa, Argentina

    Mario José Diván

  5. Department of Computing, East Tennessee State University, Johnson City, TN, USA

    Mohammed S. Khan

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Himthani, P., Dubey, G.P. (2022). Application of Machine Learning Techniques in Intrusion Detection Systems: A Systematic Review. In: Poonia, R.C., Singh, V., Singh Jat, D., Diván, M.J., Khan, M.S. (eds) Proceedings of Third International Conference on Sustainable Computing. Advances in Intelligent Systems and Computing, vol 1404. Springer, Singapore. https://doi.org/10.1007/978-981-16-4538-9_10

Download citation

Publish with us

Policies and ethics

Search

Navigation