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1
Flow-MAE: Leveraging Masked AutoEncoder for Accurate, Effic..:
, In:
Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses
,
Hang, Zijun
;
Lu, Yuliang
;
Wang, Yongjie
. - p. 297-314 , 2023
Link:
https://dl.acm.org/doi/10.1145/3607199.3607206
RT T1 <cTitle>Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses</cTitle>: T1 <Title>Flow-MAE: Leveraging Masked AutoEncoder for Accurate, Efficient and Robust Malicious Traffic Classification</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3607206&Exemplar=1&LAN=DE A1 Hang, Zijun A1 Lu, Yuliang A1 Wang, Yongjie A1 Xie, Yi PB ACM YR 2023 K1 Malicious Traffic Classification K1 Masked AutoEncoder K1 Masked Patch Model K1 Pre-training Model K1 Security and privacy K1 Network security K1 Intrusion/anomaly detection and malware mitigation K1 Intrusion detection systems K1 Computing methodologies K1 Artificial intelligence SP 297 OP 314 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3607199.3607206 DO https://dl.acm.org/doi/10.1145/3607199.3607206 SF ELIB - SuUB Bremen
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