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Android is the world’s most popular operating system, touching the lives of nearly half its population. But its scale and capabilities regularly attract criminals, fraudsters, and scammers looking to steal money from users or otherwise illicitly profit. This book’s authors have been working in the field of Android security since 2011, shortly after the first malware sample was found on the platform. Three of us—Sebastian, Salvador, and Sai—developed defenses against Android malware as part of Google’s Android Security team. Separately, V.S. and his researchers, including Qian and Yanhai, created some of the first robust machine learning methods for characterizing the behavior of Android malware. Over the years, we’ve seen Android users wrestle with ransomware, phishing, fraud, and many other kinds of harmful applications. We’ve also seen malware developers become more sophisticated, producing interesting families of malicious apps that have been documented by security researchers all over the world. After observing and combating these digital threats for more than a decade, we decided it was time to record what we knew about the Android malware we’d seen in the past, the methods of analyzing the malware of the present, and the use of machine learning to detect malware that may appear in the future. To date, this book is the most comprehensive overview of Android malware trends. Its aim is to help readers develop the analysis and detection skills that are so vital in today’s cybersecurity landscape. As you begin to study the vast array of malware categories in the Android ecosystem, you’ll soon realize how dynamic they are and how complex malware detection can be. Most security books don’t touch on machine learning techniques, but over the last few years machine learning algorithms have proven effective at accelerating the identification and classification of malware apps, allowing defenders to respond more quickly to that complexity and at a larger scale. Developing expertise in this type of AIpowered automation is a natural next step in the evolution of a malware analyst’s skill set. This book is for three kinds of readers. First, it is for those who seek to understand what mobile malware looks like and how to inspect it. Second, it is for more experienced Android malware analysts looking for a thorough picture of the Android malware ecosystem. We cover numerous real Android malware specimens, including noteworthy malware families that have never before been publicly discussed. Third, this book is for security professionals interested in familiarizing themselves with the use of machine learning to detect malware. By considering the objectives and functionality of different malware categories, you’ll learn how to leverage machine learning algorithms to detect new malware at scale.