Autoencoders for Anomaly Detection

Autoencoders are like a special algorithm in the Neural Network family. They’re part of the unsupervised learning squad. In simple terms, they learn to turn raw information into an encoded code and then quickly flip it back to cross-check if everything matches up or not. Some of the key-components of autoencoder is discussed below which are used in anomaly detection:

• Triple layering: Autoencoders wear a three-layered cape. There’s the encoder layer, the bottleneck layer (sounds fancy but it is the magical layer) and the decoder layer. The encoder does the starting job of squishing the input data into a smaller encoded data, capturing the complex patterns of features in the data. The bottleneck layer or the latent space is a critical component which represents the compressed form of the input data and acts as a feature space where anomalies are expected to be less well-represented. Finally, the decoder layer reconstructs the input data from the compressed representation which was previously created by the encoder.
• Efficient Training Process: Autoencoders are trained on a dataset containing predominantly normal instances. The model learns to encode and reconstruct this normal data accurately.
• Anomaly Detector: After all that training, it can spot anomalies. It does this by turning data into input data and comparing them. There’s this thing called the “reconstruction error” – basically, how much the reconstructed data differs from the original. If the difference is too big, it’s a red alert – an anomaly!

With the advancement of technology there is also a signification increment of frauds. In modern days, frauds are very common in monetary departments. Let’s assume we have an efficient algorithm which observes data flow actions, learns the patterns and can even predict which are the anomalies or frauds. This efficient algorithm can be the autoencoders which are designed to learn from a bunch of examples without someone telling it what’s normal or anomaly. In this article, we will explore the use of autoencoders in anomaly detection and implement it to detect anomaly within the dataset.