Skip to main content
SpringerLink
Log in

Enhanced intrusion detection system via agent clustering and classification based on outlier detection

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

The rapid evolution of cloud computing technology has enabled seamless connection of devices on a broad spectrum. Also, it enables storage of massive quantity of data in the form of data centers. This suggests a shared pool of resources where users situated all over the world have access to the aforementioned data centers. Such a framework has cyber-security based challenges where it becomes vulnerable to external attacks. There arises a need for an Intrusion Detection System (IDS) to prevent the system from unwanted and malicious attacks. However, the existing IDS have not been able to efficiently combinehigh accuracy with low complexity and time efficiency. In order to overcome these challenges an Enhanced Intrusion Detection System via Agent Clustering and Classification based on Outlier Detection (EIDS-ACC-OD) is proposed. At first, preprocessing is performed to remove unwanted spaces using outlier detection. Then modified K-means clustering algorithm is developed for data segmentation. Further, K-Nearest Neighbor (KNN) is utilized for categorization of the attacks.

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

Similar content being viewed by others

References

  1. Ali M et al (2015) Security in cloud computing: opportunities and challenges. Inf Sci 305:357–383

    Article  MathSciNet  Google Scholar 

  2. Javaid A et al (2016) A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI international conference on bio-inspired information and communications technologies (formerly BIONETICS), pp. 21–26

  3. Aljawarneh S et al (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J Comput Sci 25:152–160

    Article  Google Scholar 

  4. Faisal MA et al (2015) Data-stream-based intrusion detection system for advanced metering infrastructure in smart grid: a feasibility study. IEEE Syst J 9:31–44

    Article  Google Scholar 

  5. Rajendran PK et al (2015) Hybrid intrusion detection system for private cloud: a systematic approach. Procedia Comput Sci 48:325–329

    Article  Google Scholar 

  6. Narudin FA et al (2016) Evaluation of machine learning classifiers for mobile malware detection. Soft Comput 20:343–357

    Article  Google Scholar 

  7. Shimna KM (2015) Continuous outlier detection based on sliding window on continuous data streams. Int J Sci Res 4

  8. Sadiq AS et al (2018) An efficient IDS using hybrid magnetic swarm optimization in WANETs. IEEE Access 6:29041–29053

    Article  Google Scholar 

  9. Pandeeswari N, Kumar G (2016) Anomaly detection system in cloud environment using fuzzy clustering based ANN. Mobile Netw Appl 21:494–505

    Article  Google Scholar 

  10. Rousseeuw PJ, Hubert M (2018) Anomaly detection by robust statistics. Wiley Interdiscip Rev Data Min Knowl Disc 8:e1236

    Google Scholar 

  11. Hu W et al (2018) Anomaly detection using local kernel density estimation and context-based regression. IEEE Trans Knowl Data Eng

  12. Jabez J, Muthukumar B (2015) Intrusion detection system (IDS): anomaly detection using outlier detection approach. Procedia Comput Sci 48:338–346

    Article  Google Scholar 

  13. Ravale U et al (2015) Feature selection based hybrid anomaly intrusion detection system using K means and RBF kernel function. Procedia Comput Sci 45:428–435

    Article  Google Scholar 

  14. Muda Z et al (2016) K-means clustering and naive bayes classification for intrusion detection. J IT Asia 4:13–25

    Article  Google Scholar 

  15. Mohamad Tahir H et al (2015) Hybrid machine learning technique for intrusion detection system. International Conference on Computing and Informatics, 2015

  16. Puri A, Sharma N (2017) A novel technique for intrusion detection system for network security using hybrid SVM-cart. International Journal of Engineering Development and Research (IJEDR) 5:155–161

  17. Kumar A et al (2018) A novel hybrid K-means and artificial bee colony algorithm approach for data clustering. Decis Sci Lett 7:65–76

    Article  Google Scholar 

  18. Shi Y et al (2018) Malicious domain name detection based on extreme machine learning. Neural Process Lett 48:1347–1357

    Article  Google Scholar 

  19. Abozinadah EA et al (2015) Detection of abusive accounts with Arabic tweets. Int J Knowl Eng IACSIT 1:113–119

    Article  Google Scholar 

  20. Veni RH et al (2018) Identifying malicious web links and their attack types in social networks, ed: IJSRCSEIT

  21. Anfilofiev A et al (2014) Algorithm for tuning fuzzy network attack classifiers based on invasive weed optimization. In: 2014 dynamics of systems, mechanisms and machines (dynamics), pp. 1–4

  22. Meti N et al (2017) Detection of distributed denial of service attacks using machine learning algorithms in software defined networks. In: 2017 international conference on advances in computing, communications and informatics (ICACCI), pp. 1366–1371

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Pondicherry Engineering College, Puducherry, India

    S. Sandosh & G. Akila

  2. Department of Information Technology, Pondicherry Engineering College, Puducherry, India

    V. Govindasamy

Authors
  1. S. Sandosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Govindasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. Akila
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Sandosh.

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

Sandosh, S., Govindasamy, V. & Akila, G. Enhanced intrusion detection system via agent clustering and classification based on outlier detection. Peer-to-Peer Netw. Appl. 13, 1038–1045 (2020). https://doi.org/10.1007/s12083-019-00822-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-019-00822-3

Keywords

Search

Navigation