Skip to main content
Log in

Intrusion Attack Detection Using Firefly Optimization Algorithm and Ensemble Classification Model

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In recent decades, the Internet of Things (IoTs) based network intrusion detection (ID) remains a challenging research topic. Currently, several machine-learning methodologies are extensively used for network ID. Most of the existing methodologies failed to obtain consistent performance in multiple class classification. In this research article, a new ID system is implemented for detecting network intrusions more efficiently. After acquiring the data from UNSW-NB15 and NSL-KDD datasets, the data denoising techniques like min–max scalar and adaptive synthetic sampling are utilized to address the data imbalancing problems. Then, the Firefly Optimization Algorithm (FOA) is implemented to choose the optimal attributes for better Intrusion attack classification. In the final phase, the selected attributes are given as input to the ensemble classifier to classify the normal and attack labels. In this article, the ensemble classifier has four classifiers like K-nearest neighbors, support vector machine, long short term memory and the multi-layer perceptron’s. The experimental examination states that the FOA based ensemble model achieved 98.89% and 98.41% of detection rate on the UNSW-NB15 and NSL KDD.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available in the [NSL-KDD] and [UNSW-NB15] repositories. NSL-KDD: https://www.kaggle.com/datasets/hassan06/nslkdd. UNSW-NB15: https://research.unsw.edu.au/projects/unsw-nb15-dataset.

References

  1. Ghasemi, J., Esmaily, J., & Moradinezhad, R. (2020). Intrusion detection system using an optimized kernel extreme learning machine and efficient features. Sādhanā, 45(1), 2. https://doi.org/10.1007/s12046-019-1230-x

    Article  Google Scholar 

  2. Gao, X., Shan, C., Hu, C., Niu, Z., & Liu, Z. (2019). An adaptive ensemble machine learning model for intrusion detection. IEEE Access, 7, 82512–82521. https://doi.org/10.1109/ACCESS.2019.2923640

    Article  Google Scholar 

  3. Kumar, V., Das, A. K., & Sinha, D. (2021). UIDS: A unified intrusion detection system for IoT environment. Evolutionary Intelligence, 14(1), 47–59. https://doi.org/10.1007/s12065-019-00291-w

    Article  Google Scholar 

  4. Verma, A., & Ranga, V. (2020). Machine learning based intrusion detection systems for IoT applications. Wireless Personal Communications, 111(4), 2287–2310. https://doi.org/10.1007/s11277-019-06986-8

    Article  Google Scholar 

  5. 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. https://doi.org/10.1109/ACCESS.2019.2903723

    Article  Google Scholar 

  6. Haghnegahdar, L., & Wang, Y. (2020). A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection. Neural Computing and Applications, 32(13), 9427–9441. https://doi.org/10.1007/s00521-019-04453-w

    Article  Google Scholar 

  7. Lv, L., Wang, W., Zhang, Z., & Liu, X. (2020). A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowledge-Based Systems, 195, 105648. https://doi.org/10.1016/j.knosys.2020.105648

    Article  Google Scholar 

  8. Wei, P., Li, Y., Zhang, Z., Hu, T., Li, Z., & Liu, D. (2019). An optimization method for intrusion detection classification model based on deep belief network. IEEE Access, 7, 87593–87605. https://doi.org/10.1109/ACCESS.2019.2925828

    Article  Google Scholar 

  9. Zavrak, S., & İskefiyeli, M. (2020). Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access, 8, 108346–108358. https://doi.org/10.1109/ACCESS.2020.3001350

    Article  Google Scholar 

  10. Zhou, Y., Mazzuchi, T. A., & Sarkani, S. (2020). M-AdaBoost-A based ensemble system for network intrusion detection. Expert Systems with Applications, 162, 113864. https://doi.org/10.1016/j.eswa.2020.113864

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.comnet.2019.107042

    Article  Google Scholar 

  12. Moghanian, S., Saravi, F. B., Javidi, G., & Sheybani, E. O. (2020). GOAMLP: Network intrusion detection with multilayer perceptron and grasshopper optimization algorithm. IEEE Access, 8, 215202–215213. https://doi.org/10.1109/ACCESS.2020.3040740

    Article  Google Scholar 

  13. Kareem, S. S., Mostafa, R. R., Hashim, F. A., & El-Bakry, H. M. (2022). An effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection. Sensors, 22(4), 1396. https://doi.org/10.3390/s22041396

    Article  Google Scholar 

  14. Ullah, S., Ahmad, J., Khan, M. A., Alkhammash, E. H., Hadjouni, M., Ghadi, Y. Y., Saeed, F., & Pitropakis, N. (2022). A new intrusion detection system for the Internet of Things via deep convolutional neural network and feature engineering. Sensors, 22(10), 3607. https://doi.org/10.3390/s22103607

    Article  Google Scholar 

  15. Alrayes, F. S., Maray, M., Gaddah, A., Yafoz, A., Alsini, R., Alghushairy, O., Mohsen, H., & Motwakel, A. (2022). Modeling of botnet detection using barnacles mating optimizer with machine learning model for Internet of Things environment. Electronics, 11(20), 3411. https://doi.org/10.3390/electronics11203411

    Article  Google Scholar 

  16. Dwivedi, S., Vardhan, M., Tripathi, S., & Shukla, A. K. (2020). Implementation of adaptive scheme in evolutionary technique for anomaly-based intrusion detection. Evolutionary Intelligence, 13(1), 103–117. https://doi.org/10.1007/s12065-019-00293-8

    Article  Google Scholar 

  17. 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. https://doi.org/10.1016/j.measurement.2019.107450

    Article  Google Scholar 

  18. Tao, P., Sun, Z., & Sun, Z. (2018). An improved intrusion detection algorithm based on GA and SVM. IEEE Access, 6, 13624–13631. https://doi.org/10.1109/ACCESS.2018.2810198

    Article  Google Scholar 

  19. Kunhare, N., Tiwari, R., & Dhar, J. (2020). Particle swarm optimization and feature selection for intrusion detection system. Sādhanā, 45(1), 109. https://doi.org/10.1007/s12046-020-1308-5

    Article  Google Scholar 

  20. Khare, N., Devan, P., Chowdhary, C. L., Bhattacharya, S., Singh, G., Singh, S., & Yoon, B. (2020). Smo-dnn: Spider monkey optimization and deep neural network hybrid classifier model for intrusion detection. Electronics, 9(4), 692. https://doi.org/10.3390/electronics9040692

    Article  Google Scholar 

  21. Ramaiah, M., Chandrasekaran, V., Ravi, V., & Kumar, N. (2021). An intrusion detection system using optimized deep neural network architecture. Transactions on Emerging Telecommunications Technologies, 32(4), e4221. https://doi.org/10.1002/ett.4221

    Article  Google Scholar 

  22. Chen, J., Qi, X., Chen, L., Chen, F., & Cheng, G. (2020). Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowledge-Based Systems, 203, 106167. https://doi.org/10.1016/j.knosys.2020.106167

    Article  Google Scholar 

  23. Alazzam, H., Sharieh, A., & Sabri, K. E. (2020). A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Systems with Applications, 148, 113249. https://doi.org/10.1016/j.eswa.2020.113249

    Article  Google Scholar 

  24. Wang, Z., Zeng, Y., Liu, Y., & Li, D. (2021). Deep belief network integrating improved kernel-based extreme learning machine for network intrusion detection. IEEE Access, 9, 16062–16091. https://doi.org/10.1109/ACCESS.2021.3051074

    Article  Google Scholar 

  25. Kan, X., Fan, Y., Fang, Z., Cao, L., Xiong, N. N., Yang, D., & Li, X. (2021). A novel IoT network intrusion detection approach based on adaptive particle swarm optimization convolutional neural network. Information Sciences, 568, 147–162. https://doi.org/10.1016/j.ins.2021.03.060

    Article  MathSciNet  Google Scholar 

  26. Safaldin, M., Otair, M., & Abualigah, L. (2021). Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 12(2), 1559–1576. https://doi.org/10.1007/s12652-020-02228-z

    Article  Google Scholar 

  27. Alazzam, H., Sharieh, A., & Sabri, K. E. (2022). A lightweight intelligent network intrusion detection system using OCSVM and Pigeon inspired optimizer. Applied Intelligence, 52(4), 3527–3544. https://doi.org/10.1007/s10489-021-02621-x

    Article  Google Scholar 

  28. Ahmed, I., Dahou, A., Chelloug, S. A., Al-qaness, M. A. A., & Elaziz, M. A. (2022). Feature selection model based on gorilla troops optimizer for intrusion detection systems. Journal of Sensors, 2022, 6131463. https://doi.org/10.1155/2022/6131463

    Article  Google Scholar 

  29. Imrana, Y., Xiang, Y., Ali, L., & Abdul-Rauf, Z. (2021). A bidirectional LSTM deep learning approach for intrusion detection. Expert Systems with Applications, 185, 115524. https://doi.org/10.1016/j.eswa.2021.115524

    Article  Google Scholar 

  30. Tomer, V., & Sharma, S. (2022). Detecting IoT attacks using an ensemble machine learning model. Future Internet, 14(4), 102. https://doi.org/10.3390/fi14040102

    Article  Google Scholar 

  31. Xu, W., Jang-Jaccard, J., Singh, A., Wei, Y., & Sabrina, F. (2021). Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset. IEEE Access, 9, 140136–140146. https://doi.org/10.1109/ACCESS.2021.3116612

    Article  Google Scholar 

  32. Azzaoui, H., Boukhamla, A. Z. E., Arroyo, D., & Bensayah, A. (2022). Developing new deep-learning model to enhance network intrusion classification. Evolving Systems, 13(1), 17–25. https://doi.org/10.1007/s12530-020-09364-z

    Article  Google Scholar 

  33. Dahou, A., Abd Elaziz, M., Chelloug, S. A., Awadallah, M. A., Al-Betar, M. A., Al-qaness, M. A. A., & Forestiero, A. (2022). Intrusion detection system for IoT based on deep learning and modified reptile search algorithm. Computational Intelligence and Neuroscience, 2022, 6473507. https://doi.org/10.1155/2022/6473507

    Article  Google Scholar 

  34. Wang, C., Deng, C., Yu, Z., Hui, D., Gong, X., & Luo, R. (2021). Adaptive ensemble of classifiers with regularization for imbalanced data classification. Information Fusion, 69, 81–102. https://doi.org/10.1016/j.inffus.2020.10.017

    Article  Google Scholar 

  35. Khammassi, C., & Krichen, S. (2020). A NSGA2-LR wrapper approach for feature selection in network intrusion detection. Computer Networks, 172, 107183. https://doi.org/10.1016/j.comnet.2020.107183

    Article  Google Scholar 

  36. Devan, P., & Khare, N. (2020). An efficient XGBoost–DNN-based classification model for network intrusion detection system. Neural Computing and Applications, 32(16), 12499–12514. https://doi.org/10.1007/s00521-020-04708-x

    Article  Google Scholar 

  37. Meftah, S., Rachidi, T., & Assem, N. (2019). Network based intrusion detection using the UNSW-NB15 dataset. International Journal of Computing and Digital Systems, 8(5), 478–487. https://doi.org/10.12785/ijcds/080505

    Article  Google Scholar 

  38. 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 defence applications, Ottawa, ON, Canada (pp. 1–6). IEEE. https://doi.org/10.1109/CISDA.2009.5356528

  39. He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), Hong Kong (pp. 1322–1328). IEEE. https://doi.org/10.1109/IJCNN.2008.4633969

  40. Munkhdalai, L., Munkhdalai, T., Park, K. H., Lee, H. G., Li, M., & Ryu, K. H. (2019). Mixture of activation functions with extended min–max normalization for forex market prediction. IEEE Access, 7, 183680–183691. https://doi.org/10.1109/ACCESS.2019.2959789

    Article  Google Scholar 

  41. Sennan, S., Somula, R., Luhach, A. K., Deverajan, G. G., Alnumay, W., Jhanjhi, N. Z., Ghosh, U., & Sharma, P. (2021). Energy efficient optimal parent selection based routing protocol for Internet of Things using firefly optimization algorithm. Transactions on Emerging Telecommunications Technologies, 32(8), e4171. https://doi.org/10.1002/ett.4171

    Article  Google Scholar 

  42. Fang, Q., Nguyen, H., Bui, X.-N., Nguyen-Thoi, T., & Zhou, J. (2021). Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model. Neural Computing and Applications, 33(8), 3503–3519. https://doi.org/10.1007/s00521-020-05197-8

    Article  Google Scholar 

  43. Xing, W., & Bei, Y. (2020). Medical health big data classification based on KNN classification algorithm. IEEE Access, 8, 28808–28819. https://doi.org/10.1109/ACCESS.2019.2955754

    Article  Google Scholar 

  44. Mohammadi, M., Rashid, T. A., Karim, S. H. T., Aldalwie, A. H. M., Tho, Q. T., Bidaki, M., Rahmani, A. M., & Hosseinzadeh, M. (2021). A comprehensive survey and taxonomy of the SVM-based intrusion detection systems. Journal of Network and Computer Applications, 178, 102983. https://doi.org/10.1016/j.jnca.2021.102983

    Article  Google Scholar 

  45. Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235–1270. https://doi.org/10.1162/neco_a_01199

    Article  MathSciNet  MATH  Google Scholar 

  46. Desai, M., & Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth, 4, 1–11. https://doi.org/10.1016/j.ceh.2020.11.002

    Article  Google Scholar 

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rekha Gangula.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gangula, R., Vutukuru, M.M. & Ranjeeth Kumar, M. Intrusion Attack Detection Using Firefly Optimization Algorithm and Ensemble Classification Model. Wireless Pers Commun 132, 1899–1916 (2023). https://doi.org/10.1007/s11277-023-10687-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10687-8

Keywords

Navigation