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Ransomware analysis using DEL with jet-like architecture comprising two CNN wings, a sparse AE tail, a non-linear PCA to produce a diverse feature space, and an MLP nose

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References

Original Paper

Deep Ensemble Learning with Jet-Like architecture

  1. Tail > Sparse Autoencoder
  2. Wings > Two CNNs
  3. Body > Non-Linear PCA
  4. Nose > MLP

Architectural details

CNN1 (Left Wing)

conv layers = 3
fc layers = 1
filter size = (1,4)
input channels = 1
output channels = 3
stride = 1
padding = none
output features = 15

CNN2 (Right Wing)

conv layers = 4
fc layers = 1
filter size = (1,3)
input channels = 1
output channels = 3
stride = 1
padding = none
output features = 15

Sparse AE (Tail)

linear layers = 8
dropout = 0.1
sparsity = 0.00005
output features = 15

NLPCA (Body)

input features = 108
linear layers = 3
output features = 15

MLP (Nose)

linear layers = 3

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Ransomware analysis using DEL with jet-like architecture comprising two CNN wings, a sparse AE tail, a non-linear PCA to produce a diverse feature space, and an MLP nose

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