Abstract
Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis. This approach not only considers different data distributions but also incorporates neighborhood relationships, enhancing anomaly detection accuracy. First, by utilizing the adversarial process of GANs, including the loss function and the rarity of anomaly objects, we constrain the generator to primarily fit the probability distribution of normal objects during the unsupervised training process; Subsequently, a synthetic dataset is sampled from the generator, and the local synthetic density, which is defined by measuring the inverse of the sum of distances between a data point and all objects in its synthetic neighborhood, is calculated; Finally, the objects that show substantial density deviations from the synthetic data are classified as anomaly objects. Extensive experiments on seven real-world datasets from various domains, including medical diagnostics, industrial monitoring, and material analysis, were conducted using seven state-of-the-art anomaly detection methods as benchmarks. The GALD method achieved an average AUC of 0.874 and an accuracy of 94.34%, outperforming the second-best method by 7.2% and 6%, respectively.