The GANfather: Controllable generation of malicious activity to improve
  defence systems
Ricardo Ribeiro Pereira Jacopo Bono João Tiago Ascensão David Aparício Pedro Ribeiro Pedro Bizarro
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.