Cyber threat hunting using unsupervised federated learning and adversary emulation
Approved
Classifications
MinEdu publication type
A4 Article in conference proceedings (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Other article
Host publication type
Conference platform
Publication channel information
Title of host publication
Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience (CSR), July 31-August 2, 2023, Venice, Italy
ISBN (print)
979-8-3503-1171-6
ISBN (electronic)
979-8-3503-1170-9
Name of conference
International Conference on Cyber Security and Resilience
Publisher
IEEE
Publication forum ID
5475
Publication forum level
1
Country of publication
United States
Internationality
Yes
Detailed publication information
Publication year
2023
Reporting year
2023
Page numbers
315-320
DOI
10.1109/CSR57506.2023.10224990
Language of publication
English
Co-publication information
International co-publication
No
Co-publication with a company
No
Availability
Link to online publication
Link to self-archived version
Classification and additional information
MinEdu field of science classification
213 Electronic, automation and communications engineering, electronics
Keywords
adversary emulation; Cyber threats; Federated learning; Threat actors; Threat hunting
Funding information
Funding information in the publication
This research has been funded by the European Commission grant IDUNN (101021911) and the Academy of Finland 6G Flagship (318927).
Source database ID
WoS ID
WOS:001062143200049
Scopus ID
2-s2.0-85171764656