Lessons Learned on Machine Learning for Computer Security
Daniel J. Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck
Abstract
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.
Topics & Concepts
Computer scienceAffect (linguistics)Outcome (game theory)Computer securityArtificial intelligenceMachine learningHuman–computer interactionSoftware engineeringPsychologyMathematicsCommunicationMathematical economicsAdvanced Malware Detection TechniquesNetwork Security and Intrusion DetectionAdversarial Robustness in Machine Learning