Machine Learning under Malware Attack The Beginnings of Adversarial ML Framework for Adversarial Malware Evaluation Problem Space Attacks Feature Space Attacks Closing Remarks. Machine learning has become key in supporting decision making processes across a wide array of applications ranging from autonomous vehicles to malware detection. However while highly accurate these algorithms have been shown to exhibit vulnerabilities in which they could be deceived to return preferred predictions. Therefore carefully crafted adversarial objects may impact the trust of machine learning systems compromising the reliability of their predictions irrespective of the field in which they are deployed. The goal of this book is to improve the understanding of adversarial attacks particularly in the malware context and leverage the knowledge to explore defenses against adaptive adversaries. Furthermore to study systemic weaknesses that can improve the resilience of machine learning models. About the author Raphael Labaca Castro is a computer scientist whose primary interests lie in the nexus between Machine Learning and Computer Security. He holds a PhD in Adversarial Machine Learning and currently leads an ML team in the quantum security field. .