|
ИСТИНА |
Войти в систему Регистрация |
ИСТИНА ИНХС РАН |
||
Methodology for Collecting a Training Dataset for an Intrusion Detection Modelстатья
Статья опубликована в журнале из списка RSCI Web of Science
Статья опубликована в журнале из перечня ВАК- Авторы: Getman Aleksandr Igorevich, Goryunov Maxim Nikolaevich, Matskevich Andrey Georgievich, Rybolovlev Dmitry Aleksandrovich
- Журнал: Труды Института системного программирования РАН (электронный журнал)
- Том: 33
- Номер: 5
- Год издания: 2021
- Первая страница: 83
- Последняя страница: 104
- Номер статьи: 5
- DOI: 10.15514/ispras-2021-33(5)-5
- Аннотация: The paper discusses the issues of training models for detecting computer attacks based on the use of machine learning methods. The results of the analysis of publicly available training datasets and tools for analyzing network traffic and identifying features of network sessions are presented sequentially. The drawbacks of existing tools and possible errors in the datasets formed with their help are noted. It is concluded that it is necessary to collect own training data in the absence of guarantees of the public datasets reliability and the limited use of pre-trained models in networks with characteristics that differ from the characteristics of the network in which the training traffic was collected. A practical approach to generating training data for computer attack detection models is proposed. The proposed solutions have been tested to evaluate the quality of model training on the collected data and the quality of attack detection in conditions of real network infrastructure.
- Добавил в систему: Гетьман Александр Игоревич