Efficient GAN-Based Anomaly Detection (1802.06222v2)

Abstract: Generative adversarial networks (GANs) are able to model the complex highdimensional distributions of real-world data, which suggests they could be effective for anomaly detection. However, few works have explored the use of GANs for the anomaly detection task. We leverage recently developed GAN models for anomaly detection, and achieve state-of-the-art performance on image and network intrusion datasets, while being several hundred-fold faster at test time than the only published GAN-based method.

• The paper’s main contribution is the integration of an encoder with generator and discriminator to streamline anomaly detection.
• It eliminates expensive latent recovery, achieving up to 800x–900x faster inference on MNIST and KDD99 datasets.
• The method leverages feature-matching losses to outperform conventional approaches in both image and network intrusion tasks.

Efficient GAN-Based Anomaly Detection

The paper "Efficient GAN-Based Anomaly Detection" presents a method leveraging Generative Adversarial Networks (GANs) to enhance anomaly detection tasks. Traditional GANs are known for their ability to model complex, high-dimensional distributions, primarily with applications in image generation and processing. However, the utilization of GANs for anomaly detection has been less explored. This work introduces an efficient approach to anomaly detection, integrating GAN models to achieve competitive results on both image and network intrusion datasets, while significantly reducing computation time during inference.

Core Contributions

The primary contribution of this work is the development of an anomaly detection method that leverages recently developed GAN models, specifically incorporating an encoder alongside the generator and discriminator during the training phase. This strategy, inspired by BiGAN frameworks, eliminates the need for expensive optimization procedures traditionally required to recover latent representations for anomaly detection. The simultaneous learning of the encoder ensures that an encoded latent space can be efficiently navigated during the test phase.

Methodology

The paper outlines a method where GANs are adapted to learn both an encoder $E$ and a generator $G$, along with a discriminator $D$. This tri-model architecture allows the system to forgo the computational burden of recovering latent variables during testing, maintaining efficiency. During training, the encoder $E$ maps inputs to latent representations, while the generator $G$ and discriminator $D$ function as in a typical GAN setup, optimizing the loss function $V(D, E, G)$.

An anomaly score $A(x)$ is then calculated for input samples, derived from a convex combination of reconstruction loss and discriminator-based loss, allowing differentiation between normal and anomalous data. Performance is evaluated using image (MNIST) and network intrusion (KDD99) datasets.

Experimental Results

The experimental results showcase the efficiency and efficacy of this GAN-based anomaly detection model. For the MNIST dataset, the model outperforms existing methods such as AnoGAN and variational autoencoders, demonstrating superior classification capabilities with a significantly enhanced inference speed (approximately 800x faster). The feature-matching loss variant $L_D$ demonstrated improved performance, underlining the discriminator's role in capturing informative features for anomaly detection.

In the case of the KDD99 dataset, which involves high-dimensional network activity data, the proposed model also exhibits competitive performance against contemporary approaches, achieving higher recall and maintaining faster inference times (up to 900x faster than AnoGAN). This establishes the model's applicability beyond image data, extending to other data types in cybersecurity contexts.

Implications and Future Work

This paper presents significant implications for both the theory and practice of anomaly detection. The use of GANs with jointly learned encoders represents a potent shift towards more efficient data processing workflows, highlighting the potential for GANs in real-time and large-scale anomaly detection applications. Moving forward, the authors suggest further exploration of diverse training strategies and the evaluation of encoder accuracies on detection performance. Such explorations could unearth more nuanced understandings and methodologies for GAN implementation in this domain.

In conclusion, this work effectively bridges the gap between GANs’ theoretical distribution modeling capabilities and their practical application in anomaly detection, opening avenues for future research and development in efficient machine learning systems for anomaly tasks.