Mark Stamp - Malware Analysis Using Artificial Intelligence and Deep Learning

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Artificial intelligence (AI) is changing the world as we know it. From its humble beginnings in the late 1940s as little more than an academic curiosity, AI has gone through multiple boom and bust cycles. With recent advances in machine learning (ML) and deep learning (DL), AI has finally taken root as a fundamental transformative technology. The changes wrought by AI already affect virtually every aspect of daily life, yet we are clearly only in the early stages of an AI-based revolution.

In the field of information security, there is no topic that is more significant than malware. The sheer volume of malware and the cost of dealing with its consequences are truly staggering. It is therefore timely to consider ML, DL, and AI in the context of malware analysis.

The chapters in this book apply numerous cutting-edge AI techniques to a wide variety of challenging problems in the malware domain. The book includes no less than 8 survey articles, which can serve to bring a reader quickly up to speed with the current state of the art. The heart of the book consists of 11 chapters that are tightly focused on AI-based techniques for malware analysis. We have also included 6 chapters where AI is applied to information security topics that are not strictly malware, but are closely related.

We are confident that this book will prove equally valuable to practitioners working in the trenches and to researchers at all levels. New and novel techniques as well as clever applications abound, yet we have strived to make the material accessible to the widest possible audience. It is our fervent hopeand firm beliefthat the tools and techniques presented in the chapters of this book will play a major role in taming the malware threat.

In this chapter, we discuss a variety of topics related to deep learning, with the primary focus on popular neural networking-based architectures. We survey various malware-related applications of each architecture considered. Each topic is discussed in some detail, with additional references for further reading provided in all cases.

This chapter can be viewed as a companion to the survey [], which covers classic machine learning techniques and their applications in cybersecurity research. Our focus here is on neural networks and deep learning, and with respect to applications, we focus most of our attention on malware-related topics, but we do mention other applications within the broader information security domain.