МЕТОДИКА ВИКОРИСТАННЯ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ВИЯВЛЕННЯ НОВИХ ТИПІВ МЕРЕЖЕВИХ АТАК
DOI:
https://doi.org/10.34185/1991-7848.itmm.2022.01.041Анотація
The report discusses methods for tuning the hyperparameters of an artificial neural network in a system for detecting and classifying network intrusions. Assuming that the surface of the multidimensional space of hyperparameters is convex, an algorithm is proposed that selects the optimal set of hyperparameters in the search space according to the criterion of maximum accuracy of network intrusion classification. As a result of experiments using three different network intrusion detection data sets – KDDCup 99, NSL-KDD and UNSW-NB15 - the optimal hyperparameters of the MLP neural network were found. It is shown that the proposed method for automatic tuning of neural network hyperparameters makes it possible to achieve high intrusion detection results even on the simplest neural network under the condition of low computational costs. These results are not inferior to the results of modern models, where the hyperparameters were manually selected by the researchers.
Посилання
Zhang H, Yu X, Ren P, Luo C, Min M. Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework; 2019. arXiv preprint arXiv:1901.07949.
Witten, I. H., & Mining, E. F. D. (2005). Practical machine learning tools and techniques (The Morgan Kaufmann Series in Data Management Systems). San Francisco, CA: Elsevier.
Ma, T., Wang, F., Cheng, J., Yu, Y., Chen, X.: A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks. Sensors 16(10), 1701 (2016). URL http://www.mdpi.com/1424-8220/16/10/1701
A. R. A. Yusof, N. I. Udzir, A. Selamat, H. Hamdan, and M. T. Abdullah. Adaptive feature selection for denial of services (DoS) attack. 2017 IEEE Conf. Appl. Inf. Netw. Secur. AINS 2017, vol. 2018–Janua, pp. 1–4, 2018.
D.E. Kim, M. Gofman. Comparison of shallow and deep neural networks for network intrusion detection. Proceedings of the IEEE Eighth Annual Computing and Communication Workshop and Conference, CCWC 2018, 2018-January (2018), pp. 204-208
S. Hosseini, M. Azizi. The hybrid technique for DDoS detection with supervised learning algorithms. Comput. Netw., 158 (2019), pp. 35-45, 10.1016/j.comnet.2019.04.027
G.S. Kushwah, S.T. Ali. Detecting DDoS attacks in cloud computing using ann and
black hole optimization. Proceedings of the Second International Conference
on Telecommunication and Networks, TEL-NET 2017, 2018-January (2018),
pp. 1-5, 10.1109/TEL-NET.2017.8343555
Tuan A Tang, Lotfi Mhamdi, Des McLernon, Syed Ali Raza Zaidi, and Mounir Ghogho. Deep learning approach for network intrusion detection in software defined networking. In Wireless Networks and Mobile Communications (WINCOM), 2016 International Conference on, pages 258–263. IEEE, 2016.
Belouch, M., Hadaj, S.E., and Idhammad, M. (2017). A two-stage classifier approach using RepTree algorithm for network intrusion detection. International Journal of Advanced Computer Science and Applications, 8(6), 389-394.
