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System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network
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| dc.contributor.author | Kirichek, Galina | |
| dc.contributor.author | Harkusha, Vladyslav | |
| dc.contributor.author | Timenko, Artur | |
| dc.contributor.author | Kulykovska, Nataliia | |
| dc.date.accessioned | 2020-04-14T06:15:06Z | |
| dc.date.available | 2020-04-14T06:15:06Z | |
| dc.date.issued | 2020-02-09 | |
| dc.identifier.citation | Kirichek 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.pdf | uk_UA |
| dc.identifier.issn | 1613-0073 | |
| dc.identifier.uri | http://ceur-ws.org/Vol-2546/paper09.pdf | |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3743 | |
| dc.identifier.uri | https://doi.org/10.31812/123456789/3743 | |
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| dc.description.abstract | In 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.iso | en | uk_UA |
| dc.publisher | Arnold E. Kiv, Serhiy O. Semerikov, Vladimir N. Soloviev, Andrii M. Striuk | uk_UA |
| dc.relation.ispartofseries | CEUR Workshop Proceedings;2546 | |
| dc.subject | neural network | uk_UA |
| dc.subject | learning | uk_UA |
| dc.subject | intrusion | uk_UA |
| dc.subject | anomalies detection | uk_UA |
| dc.subject | SOM | uk_UA |
| dc.title | System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network | uk_UA |
| dc.type | Article | uk_UA |
