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PhoGAD: Graph-based Anomaly Behavior Detection with Persist..:
, In:
Proceedings of the 17th ACM International Conference on Web Search and Data Mining
,
Yuan, Ziqi
;
Zhou, Haoyi
;
Chen, Tianyu
. - p. 920-929 , 2024
Link:
https://dl.acm.org/doi/10.1145/3616855.3635783
RT T1 <cTitle>Proceedings of the 17th ACM International Conference on Web Search and Data Mining</cTitle>: T1 <Title>PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3635783&Exemplar=1&LAN=DE A1 Yuan, Ziqi A1 Zhou, Haoyi A1 Chen, Tianyu A1 Li, Jianxin PB ACM YR 2024 K1 anomaly detection K1 behavior detection K1 graph learning K1 neural networks K1 persistent homology K1 Security and privacy K1 Intrusion/anomaly detection and malware mitigation K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Learning paradigms K1 Unsupervised learning K1 Anomaly detection K1 Mathematics of computing K1 Discrete mathematics K1 Graph theory K1 Graph algorithms SP 920 OP 929 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3616855.3635783 DO https://dl.acm.org/doi/10.1145/3616855.3635783 SF ELIB - SuUB Bremen
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