Autoencoders Anomaly Detection | Anomaly Detection

Introduction

Anomaly detection is a crucial task in various domains such as fraud detection, network security, and manufacturing quality control. Autoencoders, a type of neural network, are particularly effective for this purpose. They learn to compress data into a latent space and then reconstruct it, making them well-suited for identifying anomalies as deviations from normal patterns.

What is an Autoencoder?

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data. It consists of two main parts:

Encoder: This part compresses the input into a latent-space representation.
Decoder: This part reconstructs the input from the latent space representation.

For example, if the input is a high-dimensional image, the encoder might compress this image into a smaller, encoded vector. The decoder then reconstructs the original image from this vector.

Why Use Autoencoders for Anomaly Detection?

Autoencoders can be trained on normal data and learn to reconstruct it effectively. When an anomaly (an outlier) is inputted into the autoencoder, the reconstruction error will be significantly higher compared to normal data. This reconstruction error can thus be used as a metric to detect anomalies.

Steps to Implement Autoencoders for Anomaly Detection

Data Preparation
Building the Autoencoder
Training the Autoencoder
Evaluating Reconstruction Error
Detecting Anomalies

Step 1: Data Preparation

First, we need to prepare our dataset. For this tutorial, we'll use a simple dataset, such as the MNIST dataset of handwritten digits. This dataset is well-known and easy to work with.

from keras.datasets import mnist
(X_train, _), (X_test, _) = mnist.load_data()
X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.
X_train = X_train.reshape((len(X_train), np.prod(X_train.shape[1:])))
X_test = X_test.reshape((len(X_test), np.prod(X_test.shape[1:])))

Step 2: Building the Autoencoder

We'll use Keras to build our autoencoder. The encoder will compress the input data, and the decoder will reconstruct it.

from keras.layers import Input, Dense
from keras.models import Model
input_dim = X_train.shape[1]
encoding_dim = 32
input_layer = Input(shape=(input_dim,))
encoded = Dense(encoding_dim, activation='relu')(input_layer)
decoded = Dense(input_dim, activation='sigmoid')(encoded)
autoencoder = Model(input_layer, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

Step 3: Training the Autoencoder

Train the autoencoder using the normal data (in this case, the MNIST training set). We use a small number of epochs for simplicity.

autoencoder.fit(X_train, X_train,
epochs=50,
batch_size=256,
shuffle=True,
validation_data=(X_test, X_test))

Step 4: Evaluating Reconstruction Error

After training, we evaluate the reconstruction error on the test set. The reconstruction error is the difference between the input and the reconstructed output.

reconstructed = autoencoder.predict(X_test)
loss = np.mean(np.power(X_test - reconstructed, 2), axis=1)

Reconstruction error for normal data will be low, while for anomalies, it will be significantly higher.

Step 5: Detecting Anomalies

To detect anomalies, we set a threshold for the reconstruction error. Data points with an error greater than this threshold are considered anomalies.

threshold = np.percentile(loss, 95)
anomalies = X_test[loss > threshold]

Conclusion

Autoencoders are a powerful tool for anomaly detection. By training on normal data and evaluating reconstruction error, we can effectively identify outliers that deviate from normal patterns. This approach is versatile and can be applied to various types of data, including images, time series, and more.