Anomaly detection research relies on various datasets to train and evaluate algorithms. Commonly used datasets include those from real-world domains such as finance, cybersecurity, and medical diagnostics. These datasets generally contain both normal and anomalous data points, allowing researchers to effectively measure the performance of their models. The choice of dataset often depends on the specific application or industry, as different contexts present unique challenges and data characteristics.
One popular dataset in the field of anomaly detection is the KDD Cup 1999 dataset, which is derived from network intrusion detection. This dataset consists of a wide range of network traffic features and includes labeled examples of both normal and attack instances. Another commonly used dataset is the NASA's Turbofan Engine Degradation Simulation Dataset (C-MAPSS), which focuses on monitoring aircraft engine performance. This dataset contains sensor data over time, with specific failure events marked as anomalies, making it ideal for developing predictive maintenance models.
For developers interested in finance, the Credit Card Fraud Detection dataset from Kaggle is another key resource. This dataset contains transaction records with a balance of legitimate and fraudulent transactions. It allows researchers to experiment with various anomaly detection techniques to identify fraudulent activities. Similarly, the MNIST dataset, though primarily used for image classification, has been adapted for anomaly detection tasks by treating certain digits or patterns as anomalies. Overall, the choice of dataset greatly influences the effectiveness of anomaly detection solutions, making it crucial to select one that closely aligns with the target problem.