Dynamic security log processing using deep learning techniques

Dzadíková, Slavomíra; Safonov, Yehor

Dynamic security log processing using deep learning techniques

Files

184_eeict_2022.pdf(1.08 MB)

Date

2022

Authors

Dzadíková, Slavomíra

Safonov, Yehor

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

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Abstract

Recently, the number of discovered cyber attacks increases rapidly. Tools for stealing personal data, destroying systems, or controlling infrastructure become continuously sophisticated to achieve malicious aims. Companies are trying to reduce the number of risks on their assets by using various security monitoring devices and tools. SIEM solutions are used for security monitoring, allowing different logs to be correlated. They offer visibility for security teams and allow early response to attacks. The main problem of SIEM software is the implementation of log parsing, which directly influences correlation rules efficiency. Usually, the biggest limitation is parsing dynamic log structures from different event sources. The main contribution of this paper is to apply advanced deep neural networks which use attention mechanisms for efficient log content parsing and its understanding. The proposed question answering model for feature extraction from raw logs should achieve automatic log procession. Obtained results show indisputable advantages of deep attention techniques compared to the common approaches.

Keywords

Correlation, deep learning, log processing, meta key extraction, natural language processing, SIEM, question answering

Citation

Proceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers. s. 184-187. ISBN 978-80-214-6030-0
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence