Lessons Learned on Machine Learning for Computer Security
Daniel Arp
Erwin Quiring
Feargus Pendlebury
Alexander Warnecke
Fabio Pierazzi
Christian Wressnegger
Lorenzo Cavallaro
Konrad Rieck
We identify 10 generic pitfalls that can affect the experimental outcome of AI driven solutions in computer security. We find that they are prevalent in the literature and provide recommendations for overcoming them in the future.
