| dc.contributor.author | Kochurko, Pavel | |
| dc.coverage.spatial | Brest | ru_RU |
| dc.date.accessioned | 2023-11-29T08:18:22Z | |
| dc.date.available | 2023-11-29T08:18:22Z | |
| dc.date.issued | 2006 | |
| dc.identifier.citation | Kochurko, P. Fusion of Detectors on the Basis of Recirculation Neural Networks for Intrusion Detection / Pavel Kochurko // International Conference on Neural Networks and Artificial Intelligence : proceedings, Brest, 31 May – 2 June, 2006 / Edited: V. Golovko [et al.]. – Brest : BSTU, 2006. – P. 44–48 : il. – Bibliogr.: p. 48 (20 titles). | ru_RU |
| dc.identifier.uri | https://rep.bstu.by/handle/data/37364 | |
| dc.description | Кочурко Павел. Объединение детекторов на основе рециркуляционных нейронных сетей для обнаружения вторжений | ru_RU |
| dc.description.abstract | The identification of attack class plays great role in intrusion detection. In this paper the method of recognition of a class of attack by means of the cumulative classifier with nonlinear recirculation neural networks as private detectors is described, strategy of detector selection – by a relative reconstruction error, relative cost of recognition error and mutual cost of recognition error are considered. Results of experiments are compared to results of similar researches. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | BrSTU | ru_RU |
| dc.subject | intrusion detection | ru_RU |
| dc.subject | обнаружение вторжений | ru_RU |
| dc.subject | classifier | ru_RU |
| dc.subject | классификатор | ru_RU |
| dc.subject | recirculation neural networks | ru_RU |
| dc.subject | рециркуляционные нейронные сети | ru_RU |
| dc.subject | dynamic classifier selection | ru_RU |
| dc.subject | динамический выбор классификатора | ru_RU |
| dc.title | Fusion of Detectors on the Basis of Recirculation Neural Networks for Intrusion Detection | ru_RU |
| dc.title.alternative | Объединение детекторов на основе рециркуляционных нейронных сетей для обнаружения вторжений | ru_RU |
| dc.type | Научный доклад (Working Paper) | ru_RU |
