Skip to main content
SpringerLink
Log in

An optimized maximum correlation based feature reduction scheme for intrusion detection in data networks

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Wireless Networks (WNs) is a widely used technology that has found application in many fields due to their mobile and flexible nature. Many attempts have been made to secure the standard of WNs by utilizing useful security features. But due to the absence of an external robust defense mechanism such as Intrusion Detection Scheme (IDS), most of the time, the network fails to provide proper security to the application. To design an effective defense mechanism, the use of appropriate features is a must for any network. This article proposes an Optimized Maximum Correlation based Feature Reduction (OMCFR) technique for data networks. The proposed scheme utilizes maximum correlation as a major factor depending upon which individual rank is allocated to the features. The useful features are extracted using OMCFR for efficient detection. The selected features are utilized with multiclass classifier to classify the data into normal against intrusive activities. A Random Forest based multiclass classifier technique is utilized in the study. The standard dataset of Wireless Networks from the AWID family (2015), CICIDS2017 and NSL-KDD family is utilized to evaluate the proposed IDS. The results show promising performance with reduced False Positive Rate (FPR) (for NSL-KDD: 0.10, for AWID: 0.27), achieves high detection accuracy (for NSL-KDD: 99.95\(\%\), for AWID: 99.2\(\%\)) and overall time complexity (for NSL-KDD: 182.5 s, for AWID: 812.45 s).

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. Denko, M. K. (2005). Detection and prevention of denial of service (dos) attacks in mobile ad hoc networks using reputation-based incentive scheme. Journal of Systemics, Cybernetics and Informatics, 3(4), 1–9.

    Google Scholar 

  2. Di Pietro, R., Guarino, S., Verde, N. V., & Domingo-Ferrer, J. (2014). Security in wireless ad-hoc networks—A survey. Computer Communications, 51, 1–20.

    Article  Google Scholar 

  3. Kannhavong, B., Nakayama, H., Nemoto, Y., Kato, N., & Jamalipour, A. (2007). A survey of routing attacks in mobile ad hoc networks. IEEE Wireless Communications, 14(5), 85–91.

    Article  Google Scholar 

  4. Zargar, S. T., Joshi, J., & Tipper, D. (2013). A survey of defense mechanisms against distributed denial of service (ddos) flooding attacks. IEEE Communications Surveys & Tutorials, 15(4), 2046–2069.

    Article  Google Scholar 

  5. Gavel, S., Raghuvanshi, A. S., & Tiwari, S. (2021). Maximum correlation based mutual information scheme for intrusion detection in the data networks. Expert Systems with Applications, 189, 116089.

    Article  Google Scholar 

  6. Li, Y., Xu, Y., Liu, Z., Hou, H., Zheng, Y., Xin, Y., Zhao, Y., & Cui, L. (2020). Robust detection for network intrusion of industrial iot based on multi-cnn fusion. Measurement, 154, 107450.

    Article  Google Scholar 

  7. Schweitzer, N., Stulman, A., Margalit, R. D., & Shabtai, A. (2016). Contradiction based gray-hole attack minimization for ad-hoc networks. IEEE Transactions on Mobile Computing, 16(8), 2174–2183.

    Article  Google Scholar 

  8. Gavel, S., Raghuvanshi, A. S., & Tiwari, S .(2020a). A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks. International Journal of Network Management. 10.1002/nem.2144

  9. Poongodi, M., & Bose, S. (2015). A novel intrusion detection system based on trust evaluation to defend against ddos attack in manet. Arabian Journal for Science and Engineering, 40(12), 3583–3594.

    Article  Google Scholar 

  10. Eid, H. F., Salama, M. A., Hassanien, A. E., & Kim, T. (2011). Bi-layer behavioral-based feature selection approach for network intrusion classification. In International Conference on Security Technology. Springer (pp. 195–203)

  11. Gavel, S., Raghuvanshi, A. S., & Tiwari, S. (2020). A novel density estimation based intrusion detection technique with Pearson’s divergence for wireless sensor networks. ISA Transactions, 111, 180–191.

    Article  Google Scholar 

  12. Sundararajan, T., Ramesh, S., Maheswar, R., & Deepak, K. (2014). Biologically inspired artificial intrusion detection system for detecting wormhole attack in manet. Wireless Networks, 20(4), 563–578.

    Article  Google Scholar 

  13. Bostani, H., & Sheikhan, M. (2017). Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems. Soft Computing, 21(9), 2307–2324.

    Article  Google Scholar 

  14. Aljawarneh, S., Aldwairi, M., & Yassein, M. B. (2018). Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, 152–160.

    Article  Google Scholar 

  15. Aljawarneh, S., Yassein, M. B., & Aljundi, M. (2019). An enhanced j48 classification algorithm for the anomaly intrusion detection systems. Cluster Computing, 22(5), 10549–10565.

    Article  Google Scholar 

  16. Maza, S., & Touahria, M. (2019). Feature selection for intrusion detection using new multi-objective estimation of distribution algorithms. Applied Intelligence, 49(12), 4237–4257.

    Article  Google Scholar 

  17. Vijayanand, R., Devaraj, D., & Kannapiran, B. (2018). Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Computers & Security, 77, 304–314.

    Article  Google Scholar 

  18. Ghazy, R. A., El-Rabaie, E. S. M., Dessouky, M. I., El-Fishawy, N. A., & Abd El-Samie, F. E. (2020). Feature selection ranking and subset-based techniques with different classifiers for intrusion detection. Wireless Personal Communications, 111(1), 375–393.

    Article  Google Scholar 

  19. Gavel, S., Raghuvanshi, A. S., & Tiwari, S. (2021). Distributed intrusion detection scheme using dual-axis dimensionality reduction for internet of things (iot). The Journal of Supercomputing, 77, 10488–10511.

    Article  Google Scholar 

  20. Zhou, Y., Cheng, G., Jiang, S., & Dai, M .(2020) . Building an efficient intrusion detection system based on feature selection and ensemble classifier. Computer Networks p 107247

  21. Amiri, F., Yousefi, M. R., Lucas, C., Shakery, A., & Yazdani, N. (2011). Mutual information-based feature selection for intrusion detection systems. Journal of Network and Computer Applications, 34(4), 1184–1199.

    Article  Google Scholar 

  22. Abraham, A., Jain, R., Thomas, J., & Han, S. Y. (2007). D-scids: Distributed soft computing intrusion detection system. Journal of Network and Computer Applications, 30(1), 81–98.

    Article  Google Scholar 

  23. Chebrolu, S., Abraham, A., & Thomas, J. P. (2005). Feature deduction and ensemble design of intrusion detection systems. Computers & Security, 24(4), 295–307.

    Article  Google Scholar 

  24. Mukkamala, S., & Sung, AH .(2005) . Significant feature selection using computational intelligent techniques for intrusion detection. In Advanced Methods for Knowledge Discovery from Complex Data. Springer (pp. 285–306)

  25. Chen, Y., Abraham, A., & Yang, B. (2006). Feature selection and classification using flexible neural tree. Neurocomputing, 70(1–3), 305–313.

    Article  Google Scholar 

  26. 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  Google Scholar 

  27. Gogoi, P., Bhuyan, M. H., Bhattacharyya, D., & Kalita, J. K. (2012). Packet and flow based network intrusion dataset. In International Conference on Contemporary Computing. Springer (pp. 322–334)

  28. Chitrakar, R., & Huang, C. (2014). Selection of candidate support vectors in incremental svm for network intrusion detection. Computers & Security, 45, 231–241.

    Article  Google Scholar 

  29. Aburomman, A. A., & Reaz, M. B. I. (2016). A novel svm-knn-pso ensemble method for intrusion detection system. Applied Soft Computing, 38, 360–372.

    Article  Google Scholar 

  30. Kuang, F., Xu, W., & Zhang, S. (2014). A novel hybrid kpca and svm with ga model for intrusion detection. Applied Soft Computing, 18, 178–184.

    Article  Google Scholar 

  31. Sindhu, S. S. S., Geetha, S., & Kannan, A. (2012). Decision tree based light weight intrusion detection using a wrapper approach. Expert Systems with Applications, 39(1), 129–141.

    Article  Google Scholar 

  32. Xiang, C., Yong, P. C., & Meng, L. S. (2008). Design of multiple-level hybrid classifier for intrusion detection system using bayesian clustering and decision trees. Pattern Recognition Letters, 29(7), 918–924.

    Article  Google Scholar 

  33. Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.

    Article  Google Scholar 

  34. Chen, X., & Ishwaran, H. (2012). Random forests for genomic data analysis. Genomics, 99(6), 323–329.

    Article  Google Scholar 

  35. Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104.

    Article  Google Scholar 

  36. Ali, E., Elazim, S. A., & Abdelaziz, A. (2017). Ant lion optimization algorithm for optimal location and sizing of renewable distributed generations. Renewable Energy, 101, 1311–1324.

    Article  Google Scholar 

  37. Mirjalili, S., Jangir, P., & Saremi, S. (2017). Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems. Applied Intelligence, 46(1), 79–95.

    Article  Google Scholar 

  38. Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222.

    Article  Google Scholar 

  39. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the kdd cup 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE (pp. 1–6)

  40. Kolias, C., Kambourakis, G., Stavrou, A., & Gritzalis, S. (2015). Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset. IEEE Communications Surveys & Tutorials, 18(1), 184–208.

    Article  Google Scholar 

  41. Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. In ICISSP (pp. 108–116)

  42. Ambusaidi, M. A., He, X., Nanda, P., & Tan, Z. (2016). Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Transactions on Computers, 65(10), 2986–2998.

    Article  MathSciNet  MATH  Google Scholar 

  43. Usha, M., & Kavitha, P. (2017). Anomaly based intrusion detection for 802.11 networks with optimal features using svm classifier. Wireless Networks, 23(8), 2431–2446.

    Article  Google Scholar 

  44. Levin, I. (2000). Kdd-99 classifier learning contest llsoft’s results overview. ACM SIGKDD Explorations Newsletter, 1(2), 67–75.

    Article  Google Scholar 

  45. Agarwal, R., & Joshi, M. V. (2001) . Pnrule: a new framework for learning classifier models in data mining (a case-study in network intrusion detection). In Proceedings of the 2001 SIAM International Conference on Data Mining SIAM, pp. 1–17

  46. Kim, D. S., & Park, J. S. (2003) . Network-based intrusion detection with support vector machines. In International Conference on Information Networking (pp. 747–756). Springer

  47. Xuren, W., Famei, H., & Rongsheng, X. (2006). Modeling intrusion detection system by discovering association rule in rough set theory framework. In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA’06). IEEE (pp. 24–24)

  48. Toosi, A. N., & Kahani, M. (2007). A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers. Computer Communications, 30(10), 2201–2212.

    Article  Google Scholar 

  49. Ashfaq, R. A. R., Wang, X. Z., Huang, J. Z., Abbas, H., & He, Y. L. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378, 484–497.

    Article  Google Scholar 

  50. Tama, B. A., Comuzzi, M., & Rhee, K. H. (2019). Tse-ids: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE Access, 7, 94497–94507.

    Article  Google Scholar 

  51. Attak, H., Combalia, M., Gardikis, G., Gastón, B., Jacquin, L., Katsianis, D., Litke, A., Papadakis, N., Papadopoulos, D., Pastor, A., et al. (2018). Application of distributed computing and machine learning technologies to cybersecurity. Space, 2, I2CAT.

    Google Scholar 

  52. Bansal, A., & Kaur, S .(2018) . Extreme gradient boosting based tuning for classification in intrusion detection systems. In International Conference on Advances in Computing and Data Sciences. Springer (pp. 372–380)

  53. Catillo, M., Rak, M., & Villano, U .(2019) .Discovery of dos attacks by the zed-ids anomaly detector. Journal of High Speed Networks(Preprint):1–17

  54. Zhong, Y., Chen, W., Wang, Z., Chen, Y., Wang, K., Li, Y., Yin, X., Shi, X., Yang, J., & Li, K. (2020). Helad: A novel network anomaly detection model based on heterogeneous ensemble learning. Computer Networks, 169, 107049.

    Article  Google Scholar 

  55. Asad, M., Asim, M., Javed, T., Beg, M. O., Mujtaba, H., & Abbas, S .(2019). Deepdetect: Detection of distributed denial of service attacks using deep learning. The Computer Journal, 63(7), 983–994

  56. Kim, T., & Pak, W. (2022). Real-time network intrusion detection using deferred decision and hybrid classifier. Future Generation Computer Systems, 132, 51–66.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication, National Institute of Technology Raipur, Raipur, India

    Shashank Gavel & Ajay Singh Raghuvanshi

  2. Department of Electronics and Communication, Motilal Nehru National Institute of Technology Allahabad India, Prayagraj, India

    Ajay Singh Raghuvanshi & Sudarshan Tiwari

Authors
  1. Shashank Gavel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajay Singh Raghuvanshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sudarshan Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashank Gavel.

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

Gavel, S., Raghuvanshi, A.S. & Tiwari, S. An optimized maximum correlation based feature reduction scheme for intrusion detection in data networks. Wireless Netw 28, 2609–2624 (2022). https://doi.org/10.1007/s11276-022-02988-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-02988-w

Keywords

Search

Navigation