MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks (1901.04997v1)

Abstract: The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems. The rich sensor data can be continuously monitored for intrusion events through anomaly detection. However, conventional threshold-based anomaly detection methods are inadequate due to the dynamic complexities of these systems, while supervised machine learning methods are unable to exploit the large amounts of data due to the lack of labeled data. On the other hand, current unsupervised machine learning approaches have not fully exploited the spatial-temporal correlation and other dependencies amongst the multiple variables (sensors/actuators) in the system for detecting anomalies. In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs). Instead of treating each data stream independently, our proposed MAD-GAN framework considers the entire variable set concurrently to capture the latent interactions amongst the variables. We also fully exploit both the generator and discriminator produced by the GAN, using a novel anomaly score called DR-score to detect anomalies by discrimination and reconstruction. We have tested our proposed MAD-GAN using two recent datasets collected from real-world CPS: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) datasets. Our experimental results showed that the proposed MAD-GAN is effective in reporting anomalies caused by various cyber-intrusions compared in these complex real-world systems.

An Overview of MAD-GAN: Multivariate Anomaly Detection for Time Series Data

The paper "MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks" presents a method for detecting anomalies in multivariate time series data using a Generative Adversarial Network (GAN) framework. This approach addresses the challenges posed by the dynamic and complex nature of Cyber-Physical Systems (CPSs), such as those found in smart buildings and water treatment facilities, where traditional anomaly detection methods often fall short.

Methodology

MAD-GAN leverages GANs to model the intricate temporal dependencies found in multivariate time series data. The framework employs Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) for both the generator and discriminator components, enabling the capture of spatial-temporal correlations and interactions among variables. This is a departure from simple threshold-based or linear transformation approaches and allows the model to handle the non-linearity intrinsic to CPS data.

The anomaly detection strategy integrates two complementary aspects:

1. Discrimination-Based Detection: Utilizes the GAN's discriminator to differentiate real from fake data, capitalizing on its adversarially trained sensitivity to anomalies.
2. Reconstruction-Based Detection: Uses the generator to map data from a latent space back to the observed data space, allowing the detection of anomalies through reconstruction residuals.

A novel Discrimination and Reconstruction Anomaly Score (DR-Score) is introduced, combining these two perspectives to effectively identify anomalous behaviors.

Experimental Evaluation

The authors validate MAD-GAN's effectiveness through experiments on two datasets: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) systems. These real-world datasets include sensor and actuator readings from CPS environments subjected to numerous simulated cyber-attacks and present both dynamic complexity and an inherent lack of labeled anomaly data.

MAD-GAN demonstrates superior performance over several unsupervised anomaly detection methods, including PCA, KNN, FB, and AE. Notably, it achieves high recall rates, which is crucial for detecting cyber intrusions where missing an anomaly might have severe consequences.

Implications and Future Work

The adoption of GANs for time-series anomaly detection reflects a trend towards employing deep learning models capable of capturing complex data dependencies and non-linearities. MAD-GAN transcends conventional methods by providing a structured means to generate realistic multivariate sequences, showing particular promise in contexts where the temporal dynamics of the system are critical.

Future research could explore enhancing model stability during training and determining optimal subsequence lengths, which greatly influence the model's performance. Exploring MAD-GAN's application beyond CPSs to domains like predictive maintenance or financial fraud detection would also be a valuable avenue of inquiry, potentially leading to significant advancements in anomaly detection methodologies.

In summary, this paper contributes a sophisticated approach to detecting anomalies in multivariate time series—an area with substantial practical significance in safeguarding critical infrastructure and enhancing automated monitoring systems. The results are promising, suggesting that GAN-based methods, especially those integrating discrimination and reconstruction pathways, can play a pivotal role in advancing unsupervised anomaly detection.