# One-Class Adversarial Nets for Fraud Detection

arXiv: https://arxiv.org/abs/1803.01798 · HTML: https://mlsanity.com/paper/1803.01798/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2018.

## Built on by (most cited first, 30 of 33)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Explainable Deep Few-shot Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/2108.00462/) (2021)
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- [Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation](https://mlsanity.com/paper/2112.10063/) (2021)
- [Deep Graph Anomaly Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2409.09957/) (2024)
- [Generative Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2402.11887/) (2024)
- [MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning](https://mlsanity.com/paper/2310.09831/) (2023)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [Elsa: Energy-based learning for semi-supervised anomaly detection](https://mlsanity.com/paper/2103.15296/) (2021)
- [Challenges and Complexities in Machine Learning based Credit Card Fraud Detection](https://mlsanity.com/paper/2208.10943/) (2022)
- [Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection](https://mlsanity.com/paper/2108.02501/) (2021)
- [Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection](https://mlsanity.com/paper/2512.18133/) (2025)
- [Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators](https://mlsanity.com/paper/2101.04645/) (2021)
- [Adversarial machine learning for protecting against online manipulation](https://mlsanity.com/paper/2111.12034/) (2021)
- [AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection](https://mlsanity.com/paper/2305.16497/) (2023)
- [GADY: Unsupervised Anomaly Detection on Dynamic Graphs](https://mlsanity.com/paper/2310.16376/) (2023)
- [Anomaly Detection in Graph Structured Data: A Survey](https://mlsanity.com/paper/2405.06172/) (2024)
- [Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection](https://mlsanity.com/paper/2112.05640/) (2021)
- [Targeted collapse regularized autoencoder for anomaly detection: black hole at the center](https://mlsanity.com/paper/2306.12627/) (2023)
- [Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly](https://mlsanity.com/paper/2405.18845/) (2024)
- [Unsupervised Learning of Multi-level Structures for Anomaly Detection](https://mlsanity.com/paper/2104.12102/) (2021)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem](https://mlsanity.com/paper/2104.03428/) (2021)
- [DeepTaskAPT: Insider APT detection using Task-tree based Deep Learning](https://mlsanity.com/paper/2108.13989/) (2021)
- [Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks](https://mlsanity.com/paper/2111.00267/) (2021)
- [Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation](https://mlsanity.com/paper/2207.10324/) (2022)
- [Credit card fraud detection - Classifier selection strategy](https://mlsanity.com/paper/2208.11900/) (2022)
- [Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities](https://mlsanity.com/paper/2210.17440/) (2022)
- [AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling](https://mlsanity.com/paper/2305.18389/) (2023)
- [Towards efficient deep autoencoders for multivariate time series anomaly detection](https://mlsanity.com/paper/2403.02429/) (2024)

## Abstract (arXiv)

Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection using training data with only benign users. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator with a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.