Self-Training of Cyber-Threat Classification Model With Threat-Payload Centric Augmentation

Deep learning (DL)-based threat classification has been investigated for effective analysis of threat events to minimize the human's resources in security operation centers (SOC). However, human-labeling (HL) by SOC security analysts is still necessary for accurate classification and responses...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 20; H. 10; S. 11740 - 11750
Hauptverfasser:	Kim, Jae-Yeol, Kwon, Hyuk-Yoon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.10.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Classification Classification algorithms Data augmentation Data models Encoding Labeling Long short term memory Payloads Pseudolabeling Security self-training Threat assessment threat classification threat-payload augmentation
ISSN:	1551-3203, 1941-0050
Online-Zugang:	Volltext
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