Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series (1809.04758v3)

Abstract: Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generate large amounts of data streams that can be continuously monitored for intrusion events. Unsupervised machine learning techniques can be used to model the system behaviour and classify deviant behaviours as possible attacks. In this work, we proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex networked CPSs. We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS. Instead of treating each sensor's and actuator's time series independently, we model the time series of multiple sensors and actuators in the CPS concurrently to take into account of potential latent interactions between them. To exploit both the generator and the discriminator of our GAN, we deployed the GAN-trained discriminator together with the residuals between generator-reconstructed data and the actual samples to detect possible anomalies in the complex CPS. We used our GAN-AD to distinguish abnormal attacked situations from normal working conditions for a complex six-stage Secure Water Treatment (SWaT) system. Experimental results showed that the proposed strategy is effective in identifying anomalies caused by various attacks with high detection rate and low false positive rate as compared to existing methods.

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

The paper presented introduces an anomaly detection methodology designed specifically for Cyber-Physical Systems (CPSs) utilizing a Generative Adversarial Networks-based framework (GAN-AD). These systems, integral to infrastructures such as water treatment plants, power grids, and autonomous vehicles, involve complex interactions among their networked components, making them susceptible to complex cyber-attacks. The proposed GAN-AD method leverages the ability of GANs to model distributions of multivariate time series data through adversarial training.

In contrast to conventional anomaly detection approaches such as Statistical Process Control (SPC) methods—like CUSUM and EWMA—this research addresses the multivariate and nonlinear characteristics of CPS data that traditional methods fail to capture effectively. GANs inherently possess the ability to deal with high-dimensional data and complex data distributions, making them well-suited to CPSs where interactions between components are non-trivial.

Methodology Overview

The proposed GAN-AD framework incorporates Long-Short Term Memory Recurrent Neural Networks (LSTM-RNNs) in both its generator and discriminator components to effectively model time series data. This holistic approach captures the temporal dependencies within CPS data, distinguishing it from current approaches that treat multivariate time series independently. The generator is trained to produce synthetic data that mimics the system's normal operating conditions, while the discriminator discerns between this synthetic data and real system outputs.

A unique aspect of this approach is how anomaly detection is executed by harnessing both the generator and the discriminator of the GAN. Anomalies are identified by analyzing the residuals between real-time samples and samples generated by the trained GAN, coupled with using the discriminator's ability to classify data points as normal or anomalous. This dual-faceted examination ensures greater precision and recall in anomaly detection.

Experimental Results

The paper validates the effectiveness of GAN-AD through experiments on a secure water treatment (SWaT) system dataset, which includes various cyber-attacks. This SWaT system is a fitting subject due to its multistage setup and the integration of multiple sensor and actuator types, typical of a CPS.

Key numerical results demonstrate the superiority of GAN-AD over existing methods, including significant improvements in detection accuracy, precision, recall, and false positive rate, affirming its ability to detect anomalies with a high detection rate and low false positive rate. Specifically, the multivariate approach showcased better performance compared to traditional univariate methods, as it effectively utilized correlations among the CPS data streams.

Implications and Future Directions

This research highlights the practicality of adopting GAN-based methods to address the unique challenges of anomaly detection in CPSs. The contributions of this work suggest that GANs provide a promising foundation for developing more intelligent and adaptive anomaly detection frameworks suitable for IoT applications.

Future work could focus on refining this approach by employing a multi-GAN framework to further exploit the relational structures within CPS data streams. Additionally, incorporating feature selection strategies could enhance computational efficiency and detection accuracy, particularly in systems with highly correlated variables. Evaluating the methodology on different CPS scenarios, such as smart buildings or other urban infrastructure systems, could expand its applicability and demonstrate its adaptability across different domains.

In conclusion, the paper adds to the growing body of research on leveraging GANs for complex real-world applications, providing a robust approach to safeguarding critical infrastructure from cyber threats.