Fuzzy CNN Autoencoder for Unsupervised Anomaly Detection in Log Data

Oleg Gorokhov (), Mikhail Petrovskiy (), Igor Mashechkin and Maria Kazachuk
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Oleg Gorokhov: Computer Science Department, Lomonosov Moscow State University, MSU, Moscow 119234, Russia
Mikhail Petrovskiy: Computer Science Department, Lomonosov Moscow State University, MSU, Moscow 119234, Russia
Igor Mashechkin: Computer Science Department, Lomonosov Moscow State University, MSU, Moscow 119234, Russia
Maria Kazachuk: Computer Science Department, Lomonosov Moscow State University, MSU, Moscow 119234, Russia

Mathematics, 2023, vol. 11, issue 18, 1-23

Abstract: 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.

Keywords: machine learning; anomaly detection; convolutional neural networks; neural networks; fuzzy logic; deep learning; log mining; computer security; reliability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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