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A Bayesian LSTM Based Active Anomaly Detection Service for ..:

, In: Proceedings of the 15th Asia-Pacific Symposium on Internetware,

Wang, Chen ; Huang, Tao ; Li, Min.. - p. 407-416 , 2024

Link: https://dl.acm.org/doi/10.1145/3671016.3674818

RT 
T1 <cTitle>Proceedings of the 15th Asia-Pacific Symposium on Internetware</cTitle>: T1 <Title>A Bayesian LSTM Based Active Anomaly Detection Service for Large Online Systems</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3674818&Exemplar=1&LAN=DE
				
A1 Wang, Chen
A1 Huang, Tao
A1 Li, Min
A1 Chen, Pengfei
A1 Chen, Zhiwen
PB ACM
YR 2024
K1 Active learning
K1 Anomaly detection
K1 Deep learning
K1 LSTM
K1 Online systems
K1 Computing methodologies
K1 Machine learning
K1 Information systems
K1 World Wide Web
K1 Web mining
K1 Security and privacy
K1 Intrusion/anomaly detection and malware mitigation
K1 Intrusion detection systems
K1 Theory of computation
K1 Theory and algorithms for application domains
K1 Machine learning theory
K1 Learning paradigms
K1 Information systems applications
K1 Data mining
K1 Data stream mining
K1 Bayesian analysis
K1 Unsupervised learning
K1 Data management systems
K1 Database design and models
K1 Data model extensions
K1 Data streams
K1 Anomaly detection
SP 407
OP 416
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3671016.3674818
DO https://dl.acm.org/doi/10.1145/3671016.3674818
SF ELIB - SuUB Bremen

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