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Engineering Mathematics and Artificial Intelligence
The fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams. Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book. This book is written for researchers, practitioners, engineers, and AI consultants.
Machine Learning and Security
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for securit...
Mommy Midwife
Staying several moves ahead has kept Troy Weathers alive in the world's most explosive hot spots. So when this marine learns that Olivia Laughton is being stalked, he immediately takes her and their unborn child on the run. His instincts tell him it will require more than skill to keep them safe--and to prove just how much he wants to stay in their lives. Olivia has no problem trusting Troy to keep her safe, or following his orders. It's believing their one night together was more than just a passionate fling that seems riskiest of all. Still, with time running out and an unsuspected enemy closing in, Olivia knows Troy is her best chance at survival...and their baby's only shot at a promising future.
Game Theory and Machine Learning for Cyber Security
GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities o...
Artificial Intelligence for Cybersecurity
This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It’s not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.
The Robber Barons
“The best, the liveliest and most illuminating” account of Rockefeller, Morgan, and the other men who seized American economic power after the Civil War (The New Republic). John D. Rockefeller, J. P. Morgan, Cornelius Vanderbilt, Andrew Carnegie, E. H. Harriman, Jay Gould, Henry Clay Frick . . . their names carry a powerful historical ring, still echoing today in the countless institutions that are part of their legacy, from universities to museums to banks. But who were the people behind the legends, and how did they rise to their positions of vast wealth and influence in the latter half of the nineteenth century? The Robber Barons is a classic work on the financiers and industrialists of the Gilded Age, who shaped their own era as well as the future of the United States—“not a mere series of biographies but a genuine history” (The New York Times Book Review).
Machine Learning under Malware Attack
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.
