The GANfather: Controllable generation of malicious activity to improve defence systems. (arXiv:2307.13787v1 [cs.LG])

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 …

aid data defence detection domains false positive high laundering low machine machine learning malicious money money laundering practice systems