Publication Details

Deep learning for predictive alerting and cyber-attack mitigation

IMERI, A.; RYŠAVÝ, O. Deep learning for predictive alerting and cyber-attack mitigation. In IEEE 13th Annual Computing and Communication Workshop and Conference, CCWC 2023. Las Vegas: IEEE Computer Society, 2023. p. 476-481. ISBN: 978-3-319-93490-7.
Czech title
Hluboké učení pro prediktivní varování před kybernetickými útoky
Type
conference paper
Language
English
Authors
Imeri Arbnor, M.Eng., MSc (DIFS)
Ryšavý Ondřej, doc. Ing., Ph.D. (DIFS)
Keywords

Cyber threat intelligence, Situational awareness system, Deep residual network, Fuzzy C-means clustering.  

Abstract

The successful security management of ICT systems and services is essential for an effective cyber security posture. The main objective is to minimize and control the damage caused by cyber-attacks and incidents, to provide effective response and recovery, and to invest efforts in preventing future cyber incidents. To achieve this objective, cyber threat intelligence (CTI) is widely applied, as it is considered a crucial mechanism to proactively defend against modern and dynamically evolving cyber threats and attacks. However, there are multiple challenges in the field of CTI, as there is an enormous amount of unstructured threats data in cyberspace that needs to be collected, classified, analyzed, and shared between states, organizations, or companies. Facing this challenge, data mining techniques and machine learning algorithms are essential for providing meaningful CTI information due to their ability to extract indistinct and hidden patterns in the data. Based on data mining techniques and machine learning algorithms' potential for successfully implementing cyber threat intelligence services, this paper develops an efficient predictive alerting model in a threat intelligence engine using the Deep Residual Network (DRN) model. Further, the main goal is to compare the performance of the DRN model with other machine learning models such as Sequential Rule Mining, IntruDTree, ScaleNet, etc. According to our experimental results, the DRN outperformed other tested machine learning models by achieving better results on parameters such as precision, recall, and F-measure. 

Published
2023
Pages
476–481
Proceedings
IEEE 13th Annual Computing and Communication Workshop and Conference, CCWC 2023
ISBN
978-3-319-93490-7
Publisher
IEEE Computer Society
Place
Las Vegas
DOI
10.1109/CCWC57344.2023.10099209
UT WoS
000995182600074
EID Scopus
2-s2.0-85156192228
BibTeX 
@inproceedings{BUT185143,
  author="Arbnor {Imeri} and Ondřej {Ryšavý}",
  title="Deep learning for predictive alerting and cyber-attack mitigation",
  booktitle="IEEE 13th Annual Computing and Communication Workshop and Conference, CCWC 2023",
  year="2023",
  pages="476--481",
  publisher="IEEE Computer Society",
  address="Las Vegas",
  doi="10.1109/CCWC57344.2023.10099209",
  isbn="978-3-319-93490-7",
  url="https://www.fit.vut.cz/research/publication/12926/"
}
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