Machine Learning and Security

Preface

Machine learning is eating the world. From communication and finance to transportation, manufacturing, and even agriculture,¹ nearly every technology field has been transformed by machine learning and artificial intelligence, or will soon be.

Computer security is also eating the world. As we become dependent on computers for an ever-greater proportion of our work, entertainment, and social lives, the value of breaching these systems increases proportionally, drawing in an increasing pool of attackers hoping to make money or simply wreak mischief. Furthermore, as systems become increasingly complex and interconnected, it becomes harder and harder to ensure that there are no bugs or backdoors that will give attackers a way in. Indeed, as this book went to press we learned that pretty much every microprocessor currently in use is insecure.²

With machine learning offering (potential) solutions to everything under the sun, it is only natural that it be applied to computer security, a field which intrinsically provides the robust data sets on which machine learning thrives. Indeed, for all the security threats that appear in the news, we hear just as many claims about how A.I. can “revolutionize” the way we deal with security. Because of the promise that it holds for nullifying some of the most complex advances in attacker competency, machine learning has been touted as the technique that will finally put an end to the cat-and-mouse game between attackers and defenders. Walking ...