Fuzzy CNN Autoencoder for Unsupervised Anomaly Detection in Log Data

Gorokhov, O.; Petrovskiy, M.; Mashechkin, I.; Kazachuk, M.

Статья опубликована в высокорейтинговом журнале

Информация о цитировании статьи получена из Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 15 февраля 2024 г.

Авторы: Gorokhov Oleg, Petrovskiy Mikhail, Mashechkin Igor, Kazachuk Maria
Журнал: Mathematics
Том: 11
Номер: 18
Год издания: 2023
Издательство: MDPI
Местоположение издательства: Basel, Switzerland
Номер статьи: 3995
DOI: 10.3390/math11183995
Аннотация: Currently, the task of maintaining cybersecurity and reliability in various computer systems is relevant. This problem can be solved by detecting anomalies in the log data, which are represented as a stream of textual descriptions of events taking place. For these purposes, reduction to a One-class classification problem is used. Standard One-class classification methods do not achieve good results. Deep learning approaches are more effective. However, they are not robust to outliers and require a lot of computational effort. In this paper, we propose a new robust approach based on a convolutional autoencoder using fuzzy clustering. The proposed approach uses a parallel convolution operation to feature extraction, which makes it more efficient than the currently popular Transformer architecture. In the course of the experiments, the proposed approach showed the best results for both the cybersecurity and the reliability problems compared to existing approaches. It was also shown that the proposed approach is robust to outliers in the training set.
Добавил в систему: Горохов Олег Евгеньевич

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Fuzzy CNN Autoencoder for Unsupervised Anomaly Detection in Log Dataстатья

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