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Learning from Limited Heterogeneous Training Data: Meta-Learning for
  Unsupervised Zero-Day Web Attack Detection across Web Domains

Learning from Limited Heterogeneous Training Data: Meta-Learning for Unsupervised Zero-Day Web Attack Detection across Web Domains

7 September 2023
Peiyang Li
Ye Wang
Qi Li
Zhuotao Liu
Ke Xu
Ju Ren
Zhiying Liu
Ruilin Lin
    AAML
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Papers citing "Learning from Limited Heterogeneous Training Data: Meta-Learning for Unsupervised Zero-Day Web Attack Detection across Web Domains"

3 / 3 papers shown
Title
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic
Mateusz Gniewkowski
H. Maciejewski
T. Surmacz
Wiktor Walentynowicz
19
8
0
03 Aug 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
338
11,684
0
09 Mar 2017
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
251
31,267
0
16 Jan 2013
1