Reference: https://arxiv.org/abs/1802.06360

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DaeJin Kim
Anomaly Detection using
One-Class Neural Networks

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Table of Contents
1. Reference
2. Anomaly Detection using One-Class SVM
3. Motivation & Design model
4. Model evaluation
5. Conclusion
1. Anomaly Detection using OC-NN

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Anomaly Detection using OC-NN
Anomaly Detection using One-Class Neural Networks
Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla
ArXiv.2018
1. Reference

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Anomaly Detection using OC-NN
2. Anomaly Detection using One-Class SVM
A. Autoencoder : Extract significant features from data

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Anomaly Detection using OC-NN
2. Anomaly Detection using One-Class SVM
B. One-Class SVM : Detect outlier with predicted hyper plane with unlabeled (one-class) data
* Hinge Loss

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Anomaly Detection using OC-NN
2. Anomaly Detection using One-Class SVM
C. Hybrid Model (Anomaly Detection using One-Class SVM) : Trained Encoder + One-Class SVM
1) Train autoencoder to extract significant features
from data
2) Train OC-SVM with trained encoder’s outputs,
Detect outliers with OC-SVM

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Anomaly Detection using OC-NN
3. Motivation & Design model
Change OC-SVM to OC-NN with OC-SVM like loss function

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Anomaly Detection using OC-NN
Change OC-SVM to OC-NN with OC-SVM like loss function
Why?
“Feature extraction in hybrid approaches generic and not aware of
the anomaly detection task.”
3. Motivation & Design model

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Anomaly Detection using OC-NN
Change OC-SVM to OC-NN with OC-SVM like loss function
* add new layer and weights
3. Motivation & Design model

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Anomaly Detection using OC-NN
Change OC-SVM to OC-NN with OC-SVM like loss function
3. Motivation & Design model

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Anomaly Detection using OC-NN
Change OC-SVM to OC-NN with OC-SVM like loss function
3. Motivation & Design model
1) Train autoencoder to extract significant features
from data
2) Train OC-NN with trained encoder’s outputs,
Detect outliers with OC-NN
“Able to influence representational learning in the
hidden layers.”

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Anomaly Detection using OC-NN
4. Model evaluation
A. Datasets used in experiments
MNIST USPS CIFAR-10
PFAM

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Anomaly Detection using OC-NN
4. Model evaluation
A. Datasets used in experiments (example for MNIST dataset)
Normal Data
Outlier

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Anomaly Detection using OC-NN
4. Model evaluation
B. Feed-forward network architecture’s used in OC-NN model for experiments

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Anomaly Detection using OC-NN
4. Model evaluation
C. Decision Score Histogram of anomalous vs normal data points
Synthetic data
CIFAR-10
MNISTUSPS
PFAM

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Anomaly Detection using OC-NN
4. Model evaluation
D. Comparison between the baseline (bottom seven rows) and state-of-the-art systems (top five rows).

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Anomaly Detection using OC-NN
4. Model evaluation
D. Comparison between the baseline (bottom seven rows) and state-of-the-art systems (top five rows).
“OC-NN significantly
outperforms existing
state-of-the-art
anomaly detection
methods”

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Anomaly Detection using OC-NN
5. Conclusion
The advantage of OC-NN is that the features of the hidden layers are constructed
for the specific task of anomaly detection.
:: Neural Network detects for unpredictable data more flexibly and more efficiently
than conventional detection systems