Skip to main content
SpringerLink
Log in

Intrusion detection techniques in network environment: a systematic review

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The entire world relates to some network capabilities in some way or the other. The data transmission on the network is getting more straightforward and quicker. An intrusion detection system helps distinguish unauthorized activities or intrusions that may settle the confidentiality, integrity, or availability of a resource. Nowadays, almost all institutions are using network-related facilities like schools, banks, offices, etc. Social media has become so popular that nearly every individual belongs to a new nation called ‘Netizen.’ Several approaches have been implemented to incorporate security features in network-related issues. However, vulnerable attacks are continuous, so intrusion detection systems have been proposed to secure computer systems and networks. Network security is a piece of the most fundamental issues in Computer Network Management. Moreover, an intrusion is considered to be the most revealed dangers to security. With the evolution of the networks, intrusion detection has emerged as a crucial field in networks’ security. The main aim of this article is to deliver a systematic review of intrusion detection approaches and systems that are used in various network environments.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Aldribi, A., Traoré, I., Moa, B., & Nwamuo, O. (2020). Hypervisor-based cloud intrusion detection through online multivariate statistical change tracking. Computers & Security, 88, 101646.

    Article  Google Scholar 

  2. Alhamdoosh, M., & Wang, D. (2014). Fast decorrelated neural network ensembles with random weights. Information Sciences, 264, 104–117.

    Article  MathSciNet  MATH  Google Scholar 

  3. Alharkan, T., & Martin, P. (2012). IDSaaS: Intrusion detection system as a service in public clouds. In Proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 686–687.

  4. Besharati, E., Naderan, M., & Namjoo, E. (2019). LR-HIDS: Logistic regression host-based intrusion detection system for cloud environments. Journal of Ambient Intelligence and Humanized Computing, 10(9), 3669–3692.

    Article  Google Scholar 

  5. Boer, P. D., & Pels, M. (2005). Host-based intrusion detection systems. Amsterdam University. https://www.delaat.net/rp/2004-2005/p19/report.pdf.

  6. Chandrasekhar, A., & Raghuveer, K. (2013). An effective technique for intrusion detection using neuro-fuzzy and radial SVM classifier. Computer Networks & Communications (NetCom), 131, 499–507.

    Article  Google Scholar 

  7. Chang, H., Feng, J., & Duan, C. (2020). HADIoT: A hierarchical anomaly detection framework for IoT. IEEE Access, 8, 154530–154539.

    Article  Google Scholar 

  8. Choudhary, S., & Kesswani, N. (2018). Detection and prevention of routing attacks in internet of things. In 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 1537–1540). IEEE.

  9. Choudhary, S., & Kesswani, N. (2019). Cluster-based intrusion detection method for internet of things. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) (pp. 1–8). IEEE.

  10. Danish, S. M., Nasir, A., Qureshi, H. K., Ashfaq, A. B., Mumtaz, S & Rodriguez, J. (2018). Network intrusion detection system for jamming attack in LoRaWAN join procedure. In Proceedings of the IEEE International Conference on Communications (ICC), pp. 1–6.

  11. Elmasry, W., Akbulut, A., & Zaim, A. H. (2020). Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Computer Networks, 168, 107042.

    Article  Google Scholar 

  12. Folino, G., & Sabatino, P. (2016). Ensemble based collaborative and distributed intrusion detection systems: A survey. Journal of Network and Computer Applications, 66, 1–16.

    Article  Google Scholar 

  13. Garfinkel, T., & Rosenblum, M. (2003). A virtual machine introspection based architecture for intrusion detection. In Ndss., pp. 1–16.

  14. Hasani, S. R., Othman, Z. A., & Kahaki, S. M. (2014). Hybrid feature selection algorithm for intrusion detection system. Journal of Computer Science, 10(6), 1015.

    Article  Google Scholar 

  15. Hebbal, Y., Laniepce, S., & Menaud, J. M. (2015) Virtual machine introspection: Techniques and applications. In Proceedings of the 10th International Conference on Availability, Reliability and Security, pp. 676–685.

  16. Hore, P., Hall, L. O., & Goldgof, D. B. (2007). Single Pass fuzzy C means. In Proceedings of the IEEE International Fuzzy Systems Conference, pp. 1–7.

  17. Jin, H., Xiang, G., Zou, D., Wu, S., Zhao, F., Li, M., et al. (2013). A VMM-based intrusion prevention system in cloud computing environment. The Journal of Supercomputing, 66(3), 1133–1151.

    Article  Google Scholar 

  18. Jin, S., Diao, R., & Shen, Q. (2012). Backward fuzzy interpolation and extrapolation with multiple multi-antecedent rules. In Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1170–1177.

  19. Kesswani, N., & Agarwal, B. (2020). SmartGuard: An IoT-based intrusion detection system for smart homes. International Journal of Intelligent Information and Database Systems, 13(1), 61–71.

    Article  Google Scholar 

  20. Khan, M. A. (2016). A survey of security issues for cloud computing. Journal of Network and Computer Applications, 71, 11–29.

    Article  Google Scholar 

  21. Kim, G., Lee, S., & Kim, S. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4), 1690–1700.

    Article  MathSciNet  Google Scholar 

  22. Larson, D. (2016). Distributed denial of service attacks–holding back the flood. Network Security, 2016(3), 5–7.

    Article  Google Scholar 

  23. Li, T., Li, Q., Zhu, S., & Ogihara, M. (2002). A survey on wavelet applications in data mining. ACM SIGKDD Explorations Newsletter, 4(2), 49–68.

    Article  Google Scholar 

  24. Li, Z., Sun, W., & Wang, L. (2012). A neural network based distributed intrusion detection system on cloud platform. In Proceedings of the 2nd International Conference on Cloud Computing and Intelligence Systems, 1:75–79.

  25. Li, Y., Xu, Y., Liu, Z., Hou, H., Zheng, Y., Xin, Y., et al. (2020). Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Measurement, 154, 107450.

    Article  Google Scholar 

  26. Liao, H. J., Lin, C. H. R., Lin, Y. C., & Tung, K. Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36, 16–24.

    Article  Google Scholar 

  27. Lin, W. C., Ke, S. W., & Tsai, C. F. (2015). CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-Based Systems, 78, 13–21.

    Article  Google Scholar 

  28. Lin, Y., Zhang, Y., & Ou, Y. (2010). The design and implementation of host-based intrusion detection system. In Proceedings of the 3rd International Symposium on Intelligent Information Technology and Security Informatics, pp. 595–598.

  29. Liu, A., Yuan, Y., & Wijesekera, D., Stavrou, A. (2009). SQLProb: A proxy-based architecture towards preventing SQL injection attacks. In Proceedings of the ACM Symposium on Applied Computing., 2054–2061.

  30. Mazini, M., Shirazi, B., & Mahdavi, I. (2019). Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms. Journal of King Saud University-Computer and Information Sciences, 31(4), 541–553.

    Article  Google Scholar 

  31. Mazraeh, S., Ghanavati, M., & Neysi, S. H. (2016). Intrusion detection system with decision tree and combine method algorithm. International Academic Journal of Science and Engineering, 3(8), 21–31.

    Google Scholar 

  32. Mehnaz, S., & Bertino, E. (2017). Ghostbuster: A fine-grained approach for anomaly detection in file system accesses. In Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, pp. 3–14.

  33. Mishra, P., Varadharajan, V., Tupakula, U., & Pilli, E. S. (2018). A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Communications Surveys & Tutorials, 21(1), 686–728.

    Article  Google Scholar 

  34. Modi, C., Patel, D., Borisaniya, B., Patel, H., Patel, A., & Rajaranjan, M. (2013). A survey of intrusion detection techniques in cloud. Journal of Network and Computer Applications, 36(1), 42–57.

    Article  Google Scholar 

  35. Mukherjee, B., Heberlein, L. T., & Levitt, K. N. (1994). Network intrusion detection. IEEE Network: The Magazine of Global Internetworking, 8(3), 26–41.

    Article  Google Scholar 

  36. Nitin, T., Singh, S. R., & Singh, P. G. (2012). Intrusion detection and prevention system (IDPS) technology-network behaviour analysis system (NBAS). ISCA Journal of Engineering Sciences, 1(1), 51–56.

    Google Scholar 

  37. Omer, K. A. A., & Awn, F. A. (2015). Performance evaluation of intrusion detection systems using ANN. Egyptian Computer Science Journal, 39(4), 32–42.

    Google Scholar 

  38. Patel, A., Taghavi, M., Bakhtiyari, K., & Junior, J. C. (2013). An intrusion detection and prevention system in cloud computing: A systematic review. Journal of Network and Computer Applications, 36(1), 25–41.

    Article  Google Scholar 

  39. Pawar, S. N., & Bichkar, R. S. (2015). Genetic algorithm with variable length chromosomes for network intrusion detection. International Journal of Automation and Computing, 12(3), 337–342.

    Article  Google Scholar 

  40. Peddabachigari, S., Abraham, A., Grosan, C., & Thomas, J. (2007). Modeling intrusion detection system using hybrid intelligent systems. Journal of Network and Computer Applications, 30(1), 114–132.

    Article  Google Scholar 

  41. Sabahi, F., & Movaghar, A. (2008). Intrusion detection: A survey. In Proceedings of the International Conference on Systems and Networks Communications, pp. 23–26.

  42. Seethalakshmi, D., & Nasira, G. M. (2016). Detecting and preventing intrusion in multi-tier web applications using double guard. In Proceedings of the 3rd International Conference on Computing for Sustainable Global Development, (INDIACom). 2016.

  43. Shar, L. K., & Tan, H. B. K. (2013). Defeating SQL injection. Computer, 46(3), 69–77.

    Article  Google Scholar 

  44. Shavlik, J., & Shavlik, M. (2004). Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage. In Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 276–285.

  45. Song, H. M., Woo, J., & Kim, H. K. (2020). In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications, 21, 100198.

    Article  Google Scholar 

  46. Stavroulakis, P., & Stamp, M. (2010). Handbook of information and communication security (p. 2010). New York: Springer.

    Book  MATH  Google Scholar 

  47. Sujitha, B., & Kavitha, V. (2015). Layered approach for intrusion detection using multi-objective particle swarm optimization. International Journal of Applied Engineering Research, 10(12), 31999–32014.

    Google Scholar 

  48. Tan, P. N., Steinbach, M., & Kumar, V. (2013). Data mining cluster analysis: Basic concepts and algorithms. Introduction to Data Mining. https://cse.sc.edu/~rose/587/PDF/chap8_basic_cluster_analysis.pdf

  49. Vasilomanolakis, E., Karuppayah, S., Mühlhäuser, M., & Fischer, M. (2015). Taxonomy and survey of collaborative intrusion detection. ACM Computing Surveys, 47(4), 55.

    Article  Google Scholar 

  50. Venkatraman, S., & Alazab, M. (2018). Use of data visualisation for zero-day Malware detection. Security and Communication Networks, 2018(12), 1–13.

    Article  Google Scholar 

  51. Verma, A., & Ranga, V. (2020). CoSec-RPL: Detection of copycat attacks in RPL based 6LoWPANs using outlier analysis. Telecommunication Systems: Modelling, Analysis, Design and Management, 75:43–61.

  52. Wu, S. X., & Banzhaf, W. (2010). The use of computational intelligence in intrusion detection systems: A review. Applied Soft Computing, 10(1), 1–35.

    Article  Google Scholar 

  53. Zhang, Y., Li, P., & Wang, X. (2019). Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access, 7, 31711–31722.

    Article  Google Scholar 

  54. Zuech, R., Khoshgoftaar, T. M., & Wald, R. (2015). Intrusion detection and big Heterogeneous data: A survey. Journal of Big Data, 2, Article number: 3.

Download references

Funding

No funding was received.

Author information

Authors and Affiliations

  1. College of Business, University of Dallas, Irving, USA

    Maruthi Rohit Ayyagari

  2. Department of Computer Science and Engineering, Central University of Rajasthan, Ajmer, Rajasthan, Punjab, India

    Nishtha Kesswani

  3. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

  4. Department of Information Technology, University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Krishan Kumar

Authors
  1. Maruthi Rohit Ayyagari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nishtha Kesswani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Munish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krishan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Munish Kumar.

Ethics declarations

Conflict of interest

The author(s) declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ayyagari, M.R., Kesswani, N., Kumar, M. et al. Intrusion detection techniques in network environment: a systematic review. Wireless Netw 27, 1269–1285 (2021). https://doi.org/10.1007/s11276-020-02529-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-020-02529-3

Keywords

Search

Navigation