Anomaly Detection with Deep Learning: Recognizing the Unusual

Have you ever stumbled upon an unexpected occurrence that didn’t fit into the norm? An anomalous event is often of interest, be it for security purposes, manufacturing defects, or fraud detection. Identifying such instances requires advanced algorithms known as anomaly detection. The deep learning approach to anomaly detection holds significant promise due to its ability to learn intricate patterns from large amounts of data. In this blog post, we dive into the world of anomaly detection with deep learning.

What is Anomaly Detection?

Anomaly detection is the process of identifying rare events or observations that diverge from the expected or typical behavior. The term “anomaly” is used to describe deviant instances in various contexts such as network attacks, sensor readings, financial transactions, and more.

Traditionally, anomaly detection is performed by studying statistical distributions like Gaussian or Mahalanobis distance. Such methods may overlook complex relationships or high-dimensional data, leading to false positives or missing crucial anomalies. This is where deep learning comes into play.

What is Deep Learning?

Deep learning is a subset of machine learning, where neural networks are trained to learn and recognize patterns in data. A neural network is a set of layered nodes that receive input, process it, and generate an output. By combining many layers, deep learning models can recognize complex patterns and features automatically.

Deep learning has revolutionized the field of image and speech recognition, natural language processing, robotics, and more.

Why Use Deep Learning for Anomaly Detection?

Deep learning models excel at learning from big data sets and generalizing patterns. In the context of anomaly detection, this means that a neural network can learn from tons of normal data and detects when there are abnormalities, including noise, interference or attacks.

Deep learning models can recognize patterns that may not be apparent in traditional statistical methods. They also feature more adaptable and robust architectures, capable of handling dynamic or high-dimensional data sources.

How to Implement Deep Learning for Anomaly Detection?

Here are some of the commonly used deep learning models and techniques for anomaly detection.

1. Autoencoders: A type of neural network that attempts to reconstruct the input signal by passing it through an “encoder,” which diminishes the original dimensionality, and then an “decoder,” which recovers the original signal. When training on normal data, the autoencoder learns efficient representations of the input. At test time, when the input is anomalous, the reconstruction error from the decoder is higher than usual.

2. Recurrent Neural Networks (RNN): An extension of neural networks that consider sequential data. RNNs use the present output as input for future inputs, enabling processing of time series data signals. Anomalous signal causes RNNs to exhibit a much larger error, indicating abnormality.

3. Variational Autoencoder (VAE): A generative model that learns to generate outputs similar to the input data by mapping the input to a latent space. The VAE model infers the posterior probability of the latent space from the input and generates new instances. At test time, the VAE can generate a higher likelihood of anomalous output, indicating a deviation from normality.

Conclusion

Anomaly detection with deep learning offers superior and adaptable methods for identifying rare occurrences. The models can learn from massive amounts of data and detect outliers with high precision. Autoencoders, RNNs, and VAEs are some of the deep learning models specifically designed for this task. While there is no rigid set of rules for determining the best anomaly detection approach, deep learning proves useful and promising for recognizing the unusual.