Abstract
This paper presents a two-layer ensemble learning model stacking2 based on the Stacking framework to deal with the problems of lack of generalization ability and low detection rate of single model intrusion detection system. The stacking2 uses SAMME, GBDT, and RF to generate the primary learner in the first layer and constructs the meta learner using the logistic regression algorithm in the second layer. The meta learner learns from the class probability outputs produced by the primary learner. In order to solve “the curse of dimensionality” of intrusion detection dataset, this paper proposes the feature selection approach based on firefly algorithm (FSAFA), which is used to select the optimal feature subsets. Based on the selected optimal feature subsets, the training set and test set are reconstructed and then applied to stacking2. As a result, a FSAFA based stacking2 intrusion detection model is proposed. The UNSW-NB15 and NSL-KDD datasets are chosen to verify the effectiveness of the proposed model. The experiment results show that the stacking2 intrusion detection model has better generalization ability than the individual learner based intrusion detection models. Compared with other typical algorithms, the FSAFA based stacking2 intrusion detection model has good performance in detection rate.
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References
Anderson, J.P.: Computer security threat monitoring and surveillance. Technical report, James P. Anderson Co., Fort Washington, PA (1980)
Brugger, T.: Kdd cup ’99 dataset (network intrusion) considered harmful. Technical report, Department of Computer Science, UC Davis (2007). https://www.kdnuggets.com/news/2007/n18/4i.html
Demir, N., DALKILIÇ, G.: Modified stacking ensemble approach to detect network intrusion. Turkish J. Electr. Eng. Comput. Sci. 26(1), 418–433 (2018)
Didaci, L., Giacinto, G., Roli, F.: Ensemble learning for intrusion detection in computer networks. In: Workshop Machine Learning Methods Applications, Siena, Italy (2002)
El Farissi, I., Saber, M., Chadli, S., Emharraf, M., Belkasmi, M.G.: The analysis performance of an intrusion detection systems based on neural network. In: 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 145–151. IEEE (2016)
Gautam, S.K., Om, H.: Computational neural network regression model for host based intrusion detection system. Perspectives in Science 8, 93–95 (2016)
Horng, S.J., et al.: A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst. Appl. 38(1), 306–313 (2011)
Idowu, R.K., Muniyandi, R.C., Lateef, U.O.: Tackling the menace of curse of dimensionality in intrusion detection systems: membrane computing approach. In: Proceedings of the 2nd Interdisciplinary conference of TASUED-UCC 2016, pp. 1539–1549 (2016)
Javaid, A., Niyaz, Q., Sun, W., Alam, M.: 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 (2016)
Kevric, J., Jukic, S., Subasi, A.: An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Comput. Appl. 28(1), 1051–1058 (2016). https://doi.org/10.1007/s00521-016-2418-1
Khammassi, C., Krichen, S.: A GA-LR wrapper approach for feature selection in network intrusion detection. Comput. Secur. 70, 255–277 (2017)
Mazini, M., Shirazi, B., Mahdavi, I.: Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and adaboost algorithms. J. King Saud Univ. Comput. Inf. Sci. 31(4), 541–553 (2019)
Moustafa, N., Slay, J.: Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1–6. IEEE (2015)
Moustafa, N., Slay, J.: The evaluation of network anomaly detection systems: statistical analysis of the unsw-nb15 data set and the comparison with the kdd99 data set. Inf. Secur. J. Global Perspect. 25(1–3), 18–31 (2016)
Papamartzivanos, D., Mármol, F.G., Kambourakis, G.: Dendron: Genetic trees driven rule induction for network intrusion detection systems. Futur. Gener. Comput. Syst. 79, 558–574 (2018)
Potluri, S., Diedrich, C.: Accelerated deep neural networks for enhanced intrusion detection system. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2016)
Salo, F., Nassif, A.B., Essex, A.: Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Comput. Netw. 148, 164–175 (2019)
Saxena, H., Richariya, V.: Intrusion detection in kdd99 dataset using SVM-PSO and feature reduction with information gain. Int. J. Comput. Appl. 98(6), 25–29 (2014)
Selvakumar, B., Muneeswaran, K.: Firefly algorithm based feature selection for network intrusion detection. Comput. Secur. 81, 148–155 (2019)
Shrivas, A.K., Dewangan, A.K.: An ensemble model for classification of attacks with feature selection based on kdd99 and NSL-KDD data set. Int. J. Comput. Appl. 99(15), 8–13 (2014)
Sornsuwit, P., Jaiyen, S.: Intrusion detection model based on ensemble learning for u2r and r2l attacks. In: 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 354–359. IEEE (2015)
Sun, C., Xing, J.c., Yang, Q.l., Han, D.s.: Intrusion detection methods based on improved naive bayesian. Microcomputer Appl. 36(01), 8–10 (2017)
Wang, W., He, Y., Liu, J., Gombault, S.: Constructing important features from massive network traffic for lightweight intrusion detection. IET Inf. Secur. 9(6), 374–379 (2015)
Wu, S.X., Banzhaf, W.: The use of computational intelligence in intrusion detection systems: a review. Appl. Soft Comput. 10(1), 1–35 (2010)
Zhi, H.: Research on intrusion detection model based on dimensional reduction and improved CS-WNN. Master’s thesis, Lanzhou University (2018)
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Chen, G., Zheng, J., Yang, S., Zhou, J., Wu, W. (2022). FSAFA-stacking2: An Effective Ensemble Learning Model for Intrusion Detection with Firefly Algorithm Based Feature Selection. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_37
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