Comparative Analysis of Deep Learning Models and Preprocessing Techniques for Anomaly Detection in Syslog

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F23%3A00370775" target="_blank" >RIV/68407700:21260/23:00370775 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ICICS60529.2023.10330520" target="_blank" >https://doi.org/10.1109/ICICS60529.2023.10330520</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICICS60529.2023.10330520" target="_blank" >10.1109/ICICS60529.2023.10330520</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Analysis of Deep Learning Models and Preprocessing Techniques for Anomaly Detection in Syslog

Popis výsledku v původním jazyce

With the increasing number of cybersecurity attacks and their increasing complexity, it is necessary to adapt the detection methods to be able to prevent such attacks. The replacement of traditional detection methods based on machine learning with more advanced deep learning and neural network approaches is the crucial step for this. In this paper, we present a comparative analysis of different deep learning models for anomaly detection in syslog. We analysed existing datasets for system logs and compared several preprocessing methods. We evaluated different deep learning models on those preprocessed datasets to provide a comprehensive overview of the current state-of-the-art in cybersecurity. We achieved the best results with the CNN model with 0.999 F1-score on the BGL dataset showing the great potential in such techniques for the real-life models monitoring the system and detecting anomalies.

Název v anglickém jazyce

Comparative Analysis of Deep Learning Models and Preprocessing Techniques for Anomaly Detection in Syslog

Popis výsledku anglicky

With the increasing number of cybersecurity attacks and their increasing complexity, it is necessary to adapt the detection methods to be able to prevent such attacks. The replacement of traditional detection methods based on machine learning with more advanced deep learning and neural network approaches is the crucial step for this. In this paper, we present a comparative analysis of different deep learning models for anomaly detection in syslog. We analysed existing datasets for system logs and compared several preprocessing methods. We evaluated different deep learning models on those preprocessed datasets to provide a comprehensive overview of the current state-of-the-art in cybersecurity. We achieved the best results with the CNN model with 0.999 F1-score on the BGL dataset showing the great potential in such techniques for the real-life models monitoring the system and detecting anomalies.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/TM03000055" target="_blank" >TM03000055: Vícedimenzionální detekce a automatizovaná reakce s využitím umělé inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 14th International Conference on Information and Communication Systems (ICICS)

ISBN

979-8-3503-0786-3

ISSN

2471-125X

e-ISSN

2573-3346

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE Jordan Section

Místo vydání

—

Místo konání akce

Irbid

Datum konání akce

21. 11. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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