Abstract
With the increasing number of attacks and growing scalability of connected networks over the past few years, researchers are brought to find other alternatives to judge the relevance, severity and correlation of network attacks. The high-dimensional intrusion detection system seems a promising dynamic protection component in security fields. In this work we propose an optimized classification scheme that coordinates several techniques for generating fuzzy association rules based on a large data set. Our main task is to ameliorate the detection rate of attacks in a real-time environment by using the one-versus-one decomposition to minimize as much as possible the false alarm rate. In addition, we aim to reduce the loss of knowledge through a suitable n-dimensional overlap function in order to model the conjunction in fuzzy rules to provide enough classification accuracy. We can also opt for the aggregation method to obtain the final decision. To evaluate the performance of our approach, an experimental study is performed so as to achieve relevant results. The final outcome shows that our approach outperforms other classifiers by providing the highest detection accuracy, a low false alarm rate and time consumption.
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References
Saez, J.A., Galar, M., Luengo, J., Herrera, F.: Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition. Knowl. Inf. Syst. 38(1), 179–206 (2014)
Alcala-Fdez, J., Alcala, R., Herrera, F.: A fuzzy association rule based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans. Fuzzy Syst. 19(5), 857–872 (2011)
Elkano, M., Galar, M., Sanz, J., Bustince, H.: Fuzzy rule-based classification systems for multi-class problems using binary decomposition strategies: on the influence of n-dimensional overlap functions in the fuzzy reasoning method. Inf. Sci. 332, 94–114 (2016)
Elkano, M., Galar, M., Sanz, J.A., Fernandez, A., Barrenechea, E., Herrera, F., Bustince, H.: Enhancing multiclass classification in FARC-HD fuzzy classifier: On the synergy between-dimensional overlap functions and decomposition strategies. IEEE Trans. Fuzzy Syst. 23(5), 1562–1580 (2015)
Elhag, S., Fernandez, A., Bawakid, A., Alshomrani, S., Herrera, F.: On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst. Appl. 42(1), 193–202 (2015)
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit. 44(8), 1761–1776 (2011)
Gaied, I., Jemili, F., Korbaa, O.: Intrusion detection based on neuro-fuzzy classification. In: International Conference on Computer Systems and Applications (AICCSA 2015), vol. 5, pp. 1–8, November (2015)
Kavsek, B., Lavrac, N.: APRIORI-SD: adapting association rule learning to subgroup discovery. Appl. Artif. Intell. 20(7), 543–583 (2006)
Alcala, R., Herrera, F.: A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Trans. Fuzzy Syst. 15(4), 616–635 (2007)
Fei, B., Liu, J.: Binary tree of SVM: a new fast multiclass training and classification algorithm. IEEE Trans. Neural Netw. 17(3), 696–704 (2006)
Kohen, J.: A coefficient of agreement for nominal scale. Educ. Psychol. Measur. 20, 37–46 (1960)
Jemili, F., Zaghdoud, M., Ben Ahmed, M.: Intrusion detection based on hybrid propagation in bayesian networks. In: Proceedings of the IEEE International Conference on Intelligence and Security Informatics, pp. 137–142, Dallas (2009)
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Gaied, I., Jemili, F., Korbaa, O. (2017). A Genetic-Fuzzy Classification Approach to Improve High-Dimensional Intrusion Detection System. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_32
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DOI: https://doi.org/10.1007/978-3-319-53480-0_32
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