Anomaly detection with Wasserstein GAN

Haloui, Ilyass; Gupta, Jayant; Feuillard, Vincent

doi:10.48550/arxiv.1812.02463

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…GAN According to Dumoulin and Belghazi [21] and Uehara and Sato [22], existing GAN algorithms suffer from a vanishing gradient problem which leads to instability and model collapse due to the use of predefined adversarial loss function. Haloui and Gupta [23] used the derived approximation to the Wasserstein distance to improve the original GAN gradient-based loss function. The improved GAN algorithm is called WGAN.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction

Gazzan,

Sheldon

2023

Future Internet

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Recent ransomware attacks threaten not only personal files but also critical infrastructure like smart grids, necessitating early detection before encryption occurs. Current methods, reliant on pre-encryption data, suffer from insufficient and rapidly outdated attack patterns, despite efforts to focus on select features. Such an approach assumes that the same features remain unchanged. This approach proves ineffective due to the polymorphic and metamorphic characteristics of ransomware, which generate unique attack patterns for each new target, particularly in the pre-encryption phase where evasiveness is prioritized. As a result, the selected features quickly become obsolete. Therefore, this study proposes an enhanced Bi-Gradual Minimax (BGM) loss function for the Generative Adversarial Network (GAN) Algorithm that compensates for the attack patterns insufficiency to represents the polymorphic behavior at the earlier phases of the ransomware lifecycle. Unlike existing GAN-based models, the BGM-GAN gradually minimizes the maximum loss of the generator and discriminator in the network. This allows the generator to create artificial patterns that resemble the pre-encryption data distribution. The generator is used to craft evasive adversarial patterns and add them to the original data. Then, the generator and discriminator compete to optimize their weights during the training phase such that the generator produces realistic attack patterns, while the discriminator endeavors to distinguish between the real and crafted patterns. The experimental results show that the proposed BGM-GAN reached maximum accuracy of 0.98, recall (0.96), and a minimum false positive rate (0.14) which all outperform those obtained by the existing works. The application of BGM-GAN can be extended to early detect malware and other types of attacks.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction

Gazzan,

Sheldon

2023

Future Internet

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“…The generator generates images that closely resemble the original samples, while the discriminator distinguishes between real samples and generated ones. Building upon GAN, several variant networks have been introduced, such as AnoGAN [17], GANomaly [18], skip-GAN [19], WGAN [20], etc., all demonstrating excellent performance in anomaly detection. Nevertheless, the training process of GAN models often encounters convergence difficulties.…”

Section: Related Workmentioning

confidence: 99%

Autoencoder-Like Knowledge Distillation Network for Anomaly Detection

Xu,

Wang,

et al. 2023

IEEE Access

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Anomaly detection is a crucial research field in computer vision with diverse applications in practical scenarios. The common anomaly detection methods employed currently consist of autoencoders, generative adversarial networks, and knowledge distillation (KD) models. However, the teacher and student models in KD might not always yield distinct representations to signify anomalies due to their similar model structure and data flow. This study proposes a novel autoencoder-like KD model based on the attention mechanism for anomaly detection. The pre-trained teacher model incorporates a dual attention module as the encoder, while the student model integrates the same dual attention module as the decoder. The teacher guides the student to learn the feature knowledge of the input image. To connect the teacher-student model, a BottleNeck module is employed, converting the features extracted from the teacher model into more compact latent codes for precise restoration by the student model, thereby achieving anomaly detection. In general, the proposed model exhibits superior performance compared to other existing anomaly detection models on specific datasets. Experimental results demonstrate that the proposed model attains the state-ofthe-art (SOTA) performance in anomaly detection on the public dataset MVTec. It achieves an average AUC of 98.2% and 98.0% at sample and pixel levels, respectively.

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“…Unfortunately, GANs tend to learn the mass of the underlying multimodal distribution well, focusing less towards the low probability regions, i.e. the tails, and have discernible problems with mode collapse [20,19].…”

Section: Related Work: Boundary Generationmentioning

confidence: 99%

“…At present, generative models based on invertible residual networks, such as [14,15], are lacking for unsupervised anomaly detection [16,17]. Anomaly detection techniques show discernible limitations for detecting anomalies near the support of multimodal distributions [18,19].…”

Section: Introductionmentioning

confidence: 99%

Boundary Of Distribution Support Generator (BDSG): Sample Generation On The Boundary

Dionelis

Yaghoobi

Tsaftaris

2020

2020 IEEE International Conference on Image Processing (ICIP)

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Generative models, such as Generative Adversarial Networks (GANs), have been used for unsupervised anomaly detection. While performance keeps improving, several limitations exist particularly attributed to difficulties at capturing multimodal supports and to the ability to approximate the underlying distribution closer to the tails, i.e. the boundary of the distribution's support. This paper proposes an approach that attempts to alleviate such shortcomings. We propose an invertibleresidual-network-based model, the Boundary of Distribution Support Generator (BDSG). GANs generally do not guarantee the existence of a probability distribution and here, we use the recently developed Invertible Residual Network (IResNet) and Residual Flow (ResFlow), for density estimation. These models have not yet been used for anomaly detection. We leverage IResNet and ResFlow for Out-of-Distribution (OoD) sample detection and for sample generation on the boundary using a compound loss function that forces the samples to lie on the boundary. The BDSG addresses non-convex support, disjoint components, and multimodal distributions. Results on synthetic data and data from multimodal distributions, such as MNIST and CIFAR-10, demonstrate competitive performance compared to methods from the literature.

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Anomaly detection with Wasserstein GAN

Cited by 5 publications

References 5 publications

An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction

An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction

Autoencoder-Like Knowledge Distillation Network for Anomaly Detection

Boundary Of Distribution Support Generator (BDSG): Sample Generation On The Boundary

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