Yes, neural networks can effectively be used for anomaly detection. Anomaly detection involves identifying data points or patterns that deviate significantly from normal behavior, and neural networks excel at learning complex patterns in data. By training on normal data, neural networks can model expected behavior and flag deviations as anomalies. This approach is particularly useful in scenarios where anomalies are rare, poorly defined, or involve high-dimensional data, such as fraud detection, network intrusion detection, or industrial system monitoring.
One common method is using autoencoders, a type of neural network designed to reconstruct input data. Autoencoders are trained to compress input data into a lower-dimensional representation (encoding) and then reconstruct it. During training, the network learns to minimize reconstruction errors for normal data. When presented with anomalous data, the reconstruction error spikes because the network hasn’t learned to represent those patterns. For example, in credit card fraud detection, an autoencoder trained on legitimate transactions would struggle to reconstruct fraudulent ones, triggering an alert. Recurrent neural networks (RNNs) or transformers can also detect anomalies in time-series data, such as predicting the next value in a sequence and flagging large prediction errors.
However, neural networks for anomaly detection require careful design. Labeled anomaly data is often scarce, so models are typically trained in a semi-supervised manner using mostly normal data. Hyperparameter tuning—like the size of the encoding layer in autoencoders—is critical to avoid overfitting or underfitting. Additionally, computational costs can be high for large datasets, and interpreting why a specific data point is flagged as anomalous may be challenging. Despite these trade-offs, neural networks offer flexibility and scalability, especially when combined with techniques like clustering (e.g., using variational autoencoders) or hybrid models that integrate traditional statistical methods for improved robustness.