Adversarial Anomaly Detection

Unsupervised Adversarial Anomaly Detection

Task: Anomaly detection in multivariate time series variable.

About 150 features in 1 input sample per 1 sec.
t1: [f1, f2, ... , f150]
t2: [f1, f2, ... , f150]
...
tn: [f1, f2, ... , f150]

SEADNet

Sequence Embedding, Anomaly Detection Network

Note) I assumed you have 2 GPUs. If not, change gpu setting code.

Stacked LSTM + Deep Auto Encoder + Adversarial Discriminator + Latent Reencoder

Input

Muti-variate time series (Feature dimension: 150, 1 Event per sec)

Output

Anomaly score

Stacked LSTM

Many to One
Create sequence latent for sequence anomaly
Sequence length: 20

Deep Auto Encoder

Create local anomaly latent

Decoder

Concat sequence latent and local latent
Reconstruct using concatenated latent variable

Test

Normal: mean 0.0, stdev 1.0, 100,000 samples, sigmoid standardazation for harder discrimination
Anomaly: mean 1.0, stddev 1.0, 100 samples, sigmoid standardazation for harder discrimination
Training only normal samples
Discriminate anomaly samples at test time

STADNet

Spatio-Temporal, Anomaly Detection Network

Note) I assumed you have 2 GPUs. If not, change gpu setting code.

1D/2D Convolution + Adversarial Discriminator

Input

Muti-variate time series (Feature dimension: 150, Window size: 20)
Reshape to [B, 150, 20, 1]

Output

Anomaly score

Deep Auto Encoder

Create local anomaly latent

Decoder

Concat sequence latent and local latent
Reconstruct using concatenated latent variable

Test

Normal: mean 0.0, stdev 1.0, 100,000 samples, sigmoid standardazation for harder discrimination
Anomaly: mean 1.0, stddev 1.0, 100 samples, sigmoid standardazation for harder discrimination
Training only normal samples
Discriminate anomaly samples at test time

Always welcome a nice idea: Code or Text what ever!