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dc.contributor.authorKirichek, Galina-
dc.contributor.authorHarkusha, Vladyslav-
dc.contributor.authorTimenko, Artur-
dc.contributor.authorKulykovska, Nataliia-
dc.date.accessioned2020-04-14T06:15:06Z-
dc.date.available2020-04-14T06:15:06Z-
dc.date.issued2020-02-09-
dc.identifier.citationKirichek G. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network [Electronic resource] / Galina Kirichek, Vladyslav Harkusha, Artur Timenko, Nataliia Kulykovska // Computer Science & Software Engineering : Proceedings of the 2nd Student Workshop (CS&SE@SW 2019), Kryvyi Rih, Ukraine, November 29, 2019 / Edited by : Arnold E. Kiv, Serhiy O. Semerikov, Vladimir N. Soloviev, Andrii M. Striuk. – P. 138-148. – (CEUR Workshop Proceedings (CEUR-WS.org), Vol. 2546). – Access mode : http://ceur-ws.org/Vol-2546/paper09.pdfuk_UA
dc.identifier.issn1613-0073-
dc.identifier.urihttp://ceur-ws.org/Vol-2546/paper09.pdf-
dc.identifier.urihttp://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3743-
dc.identifier.urihttps://doi.org/10.31812/123456789/3743-
dc.description1. Akbar, S., Rao, K.N., Chandulal, J.A.: Intrusion detection system methodologies based on data analysis. International Journal of Computer Applications 5(2), 10–20 (2010). doi:10.5120/892-1266 2. Bahrololum, M., Salahi, E., Khaleghi, M.: An improved intrusion detection technique based on two strategies using decision tree and neural network. Journal of Convergence Information Technology 4(4), 96–101 (2009) 3. Chebrolu, S., Abraham, A., Thomas, J.P.: Feature deduction and ensemble design of intrusion detection systems. Computers & Security 24(4), 295–307 (2005). doi:10.1016/j.cose.2004.09.008 4. Dierbach Ch.: Python as a first programming language. Journal of Computing Sciences in Colleges 29(6), 153–154 (2014) 5. García-Teodoro, P., Díaz-Verdejoa, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security 28(1–2), 18–28 (2009). doi:10.1016/j.cose.2008.08.003 6. Imamverdiyev, Y.N., Sukhostat, L.V.: Obnaruzhenie anomalii v setevom trafike na osnove informativnykh priznakov (Network traffic anomalies detection based on informative features). Radio electronics, computer science, control 3, 113–120 (2017) doi:10.15588/1607-3274-2017-3-13 7. KDD Cup 1998 Data. http://kdd.ics.uci.edu//databases/kddcup98/kddcup98.html (1999). Accessed 21 Mar 2019 8. Kirichek, G., Kurai, V.: Implementation quadtree method for comparison of images. In: 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pp. 129–132. IEEE (2018) doi:10.1109/TCSET.2018.8336171 9. Kirichek, G., Tymoshenko, V., Rudkovskyi, O., Hrushko, S.: Decentralized System for Run Services. CEUR Workshop Proceedings 2353, 860–872 (2019) 10. Kohonen, T.: Self-Organizing Maps. Springer-Verlag, Berlin, Heidelberg (2001). doi:10.1007/978-3-642-56927-2 11. Mukkamala, S., Janoski, G., Sung, A.: Intrusion detection using neural networks and support vector machines. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02. Honolulu, HI, USA, pp. 1702–1707 (2002). doi:10.1109/IJCNN.2002.1007774 12. Ritter, H., Martinetz, T., Schulten, K., Barsky, D., Tesch, M., Kates, R.: Neural Computation and Self-Organizing Maps: An Introduction. Addison-Wesley, Reading (1992) 13. Rueckstiess T.: Python PyBrain package v0.3, pybrain.structure.modules.kohonen module source code :: PyDoc.net. http://pydoc.net/PyBrain/0.3/pybrain.structure.modules.kohonen (2009). Accessed 17 Aug 2019 14. Sabhnani, M., Serpen, G.: Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context. In: Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications. MLMTA'03, June 23–26, 2003, Las Vegas, Nevada, USA, pp. 209–215. CSREA Press (2003) 15. Salnik, S.V., Salnyk, V.V., Symonenko, O.A., Sova, O.Ya.: Metod vyiavlennia vtorhnen v mobilni radiomerezhi na osnovi neironnykh merezh (Method of intrusion detection in mobile radio networks on the basis of neurals networks). Science and Technology the Air Force of Ukraine 4(21), 82–90 (2015) 16. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Kiv, A.E.: Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. In: Kiv, A.E., Soloviev, V.N. (eds.) Proceedings of the 1st International Workshop on Augmented Reality in Education (AREdu 2018), Kryvyi Rih, Ukraine, October 2, 2018. CEUR Workshop Proceedings 2257, 122–147. http://ceur-ws.org/Vol-2257/paper14.pdf (2018). Accessed 30 Nov 2018 17. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6. IEEE (2009). doi:10.1109/CISDA.2009.5356528 18. Zhang, Z., Manikopoulos, C.: Neural networks in statistical anomaly intrusion detection. Neural network world 11(3), 305–316 (2001)-
dc.description.abstractIn this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a sufficient number of solutions and algorithms for training, designing and testing various types of neural networks. Software architecture is presented using a procedural-object approach. Because there is no need to save intermediate result of the program (after learning entire perceptron is stored in the file), all the progress of learning is stored in the normal files on hard disk.uk_UA
dc.language.isoenuk_UA
dc.publisherArnold E. Kiv, Serhiy O. Semerikov, Vladimir N. Soloviev, Andrii M. Striukuk_UA
dc.relation.ispartofseriesCEUR Workshop Proceedings;2546-
dc.subjectneural networkuk_UA
dc.subjectlearninguk_UA
dc.subjectintrusionuk_UA
dc.subjectanomalies detectionuk_UA
dc.subjectSOMuk_UA
dc.titleSystem for detecting network anomalies using a hybrid of an uncontrolled and controlled neural networkuk_UA
dc.typeArticleuk_UA
Розташовується у зібраннях:Кафедра інформатики та прикладної математики

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