Enhancing Cybersecurity Through Comparative Analysis of Deep Learning Models for Anomaly Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F24%3A00379945" target="_blank" >RIV/68407700:21260/24:00379945 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0012312800003648" target="_blank" >https://doi.org/10.5220/0012312800003648</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0012312800003648" target="_blank" >10.5220/0012312800003648</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing Cybersecurity Through Comparative Analysis of Deep Learning Models for Anomaly Detection
Original language description
With the increasing complexity of cyber attacks, traditional methods for anomaly detection in cybersecurity are insufficient, leading to the necessity of integrating deep learning and neural network approaches. This paper presents a comparative analysis of the most powerful deep learning methods for such anomaly detection. We analysed existing datasets for syslog and dataflow, compared several preprocessing methods and identified their strengths and weaknesses. Additionally, we trained and evaluated several deep learning models to provide a comprehensive overview of the current state-of-the-art in cybersecurity. The CNN model achieves excellent results, with 0.999 supervised and 0.938 semi-supervised F1-score in syslog anomaly detection on the BGL dataset and 0.985 F1-score in dataflow anomaly detection on the NIDS dataset. This research contributes to the field of cybersecurity by aiding researchers and practitioners in selecting effective deep-learning models for robust real-life anomaly detection systems. Our findings highlight the reusability of these models in real-life systems.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/TM03000055" target="_blank" >TM03000055: Multidimensional detection and automated response using artificial intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 10th International Conference on Information Systems Security and Privacy 2024
ISBN
978-989-758-683-5
ISSN
2184-4356
e-ISSN
2184-4356
Number of pages
9
Pages from-to
682-690
Publisher name
SciTePress
Place of publication
Madeira
Event location
Rome
Event date
Feb 26, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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