# The GANfather: Controllable generation of malicious activity to improve defence systems

arXiv: https://arxiv.org/abs/2307.13787 · HTML: https://mlsanity.com/paper/2307.13787/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [Scalable Graph Learning for Anti-Money Laundering: A First Look](https://mlsanity.com/paper/1812.00076/) (2018)
- [Revisiting Adversarially Learned Injection Attacks Against Recommender Systems](https://mlsanity.com/paper/2008.04876/) (2020)
- [Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity](https://mlsanity.com/paper/2005.14635/) (2020)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles](https://mlsanity.com/paper/2206.11433/) (2022)
- [GuiltyWalker: Distance to illicit nodes in the Bitcoin network](https://mlsanity.com/paper/2102.05373/) (2021)
- [Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack](https://mlsanity.com/paper/2107.10457/) (2021)
- [Detection of money laundering groups using supervised learning in networks](https://mlsanity.com/paper/1608.00708/) (2016)
- [Synthetic Data Generation for Fraud Detection using GANs](https://mlsanity.com/paper/2109.12546/) (2021)
- [CubeFlow: Money Laundering Detection with Coupled Tensors](https://mlsanity.com/paper/2103.12411/) (2021)

## Abstract (arXiv)

Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. In practice this can lead to low detection rates and high false positive rates, which characterise for example anti-money laundering systems. In fact, it is estimated that 1.7--4 trillion euros are laundered annually and go undetected. We propose The GANfather, a method to generate samples with properties of malicious activity, without label requirements. We propose to reward the generation of malicious samples by introducing an extra objective to the typical Generative Adversarial Networks (GANs) loss. Ultimately, our goal is to enhance the detection of illicit activity using the discriminator network as a novel and robust defence system. Optionally, we may encourage the generator to bypass pre-existing detection systems. This setup then reveals defensive weaknesses for the discriminator to correct. We evaluate our method in two real-world use cases, money laundering and recommendation systems. In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system. In the latter, we recommend the target item to a broad user base with as few as 30 synthetic attackers. In both cases, we train a new defence system to capture the synthetic attacks.