Adversarially Learned Anomaly Detection (1812.02288v1)

Abstract: Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task. ALAD then uses reconstruction errors based on these adversarially learned features to determine if a data sample is anomalous. ALAD builds on recent advances to ensure data-space and latent-space cycle-consistencies and stabilize GAN training, which results in significantly improved anomaly detection performance. ALAD achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published GAN-based method.

• The paper’s main contribution is presenting ALAD, a bi-directional GAN framework that significantly enhances anomaly detection performance and speed compared to previous GAN-based methods.
• It employs cycle-consistency regularization and spectral normalization to stabilize GAN training, resulting in improved sample reconstructions.
• Experimental evaluations on datasets like KDD99 and CIFAR-10 confirm ALAD's superior precision, recall, and AUROC, making it promising for real-time applications in cybersecurity and medical imaging.

Adversarially Learned Anomaly Detection

The paper "Adversarially Learned Anomaly Detection" presents a novel method for anomaly detection that leverages the potential of Generative Adversarial Networks (GANs) to model complex, high-dimensional data distributions. The proposed method, Adversarially Learned Anomaly Detection (ALAD), capitalizes on the bi-directional nature of GANs to derive features applicable for anomaly detection tasks, significantly enhancing inference efficiency and efficacy compared to existing GAN-based methods.

The research addresses the critical challenge of anomaly detection, a cornerstone problem in diverse fields such as cyber-security, fraud detection, and medical imaging. The intrinsic complexity and high dimensionality of data in these domains necessitate robust anomaly detection models. ALAD employs a bi-directional GAN architecture that learns an encoder network simultaneous with the generator, thus facilitating rapid inference abilities while retaining high anomaly detection performance.

Methodology

ALAD refines the basic GAN framework by ensuring data-space and latent-space cycle-consistencies. This approach stabilizes GAN training and enhances anomaly detection through improved sample reconstructions. The method includes several architectural augmentations:

• Encoder Network: Simultaneously learned with the generator, allowing direct mapping of data to the latent space.
• Cycle-Consistency Regularization: Employing an additional discriminator ensures the consistency of generated samples with the input data, mitigating the issue of poor reconstruction fidelity.
• Spectral Normalization: Applied to the GAN's components to stabilize training by enforcing Lipschitz constraints, ensuring more reliable and consistent model convergence.

Experimental Evaluation

ALAD's performance is rigorously evaluated against a suite of baseline methods including One-Class SVMs, Isolation Forests, and other deep learning-based models like DSEBM and DAGMM. Experiments conducted on diverse datasets, including tabular datasets like KDD99 and Arrhythmia, as well as image datasets such as SVHN and CIFAR-10, demonstrate ALAD's superior anomaly detection capabilities. Notably, ALAD achieves competitive and often superior results in terms of metrics such as precision, recall, and AUROC. Importantly, ALAD offers considerable computational benefits, being several hundred-fold faster at test time than the only other published GAN-based anomaly detection method, AnoGAN.

Implications and Future Work

The implications of ALAD are manifold. In practice, its enhanced efficiency and performance make it a promising candidate for real-time anomaly detection in environments with stringent latency demands, such as network intrusion detection or dynamic monitoring in healthcare.

From a theoretical standpoint, the integration of cycle-consistency and spectral normalization within the GAN framework broadens the horizon of stable GAN applications beyond generative tasks to discriminative problem domains. The research opens up exciting avenues for further exploration, particularly in adapting the ALAD framework for other data types, such as time-series or multi-modal data, potentially combining it with other advances in adversarial learning.

Moreover, as the GAN field evolves, particularly with innovations aimed at improving training stability and generative quality, ALAD and similar methods stand poised to benefit directly. Continued investigation into the interplay between adversarial training dynamics and anomaly detection performance will be crucial, potentially exploiting recent advancements like self-supervision or adversarial training techniques focused on interpretable representations. Thus, ALAD serves as a significant stepping stone in the ongoing refinement of AI models for robust anomaly detection in complex, high-dimensional data spaces.