This book begins with a broader discussion of cybercrime and attacks targeting ATMs and then focuses on a specific type of cybercrime named "ATM Hacking." It discusses ATM Hacking from a more full scope of aspects, including technology, modus operandi, law enforcement, socio-economic and geopolitical context, and theory development. After unpacking a classic case of ATM Hacking and its modus operandi, implications for cybersecurity and prevention, intra- and inter-agency collaboration, and theory development are presented. This book also demonstrates the analysis of extensive qualitative data collected from a high-profile case in which European criminal group hacked into a London voice mail server belonging to a Taiwanese financial institution - First Commercial Bank,. Then it programmed dozens of ATMs to "spit out" millions of dollars of cash. The successful crackdown on this type of crime is rare, if not unique, while the number of similar crimes has increased enormously in recent years and the trend seem to continue unabatingly. Further, the implications go beyond a country or a continent. Intra- and inter-agency collaboration among players of law enforcement is essential to the case especially in the police context of "turf jealousies." The authors seek to document the ways in which agencies collaborate, as well as the perceived benefits and challenges of cooperation. Whether the broader political and contextual climates in which these agencies operate, limit the extent to which they can cooperate. This book is useful as a reference for researchers and professionals working in the area of cybercrime and cybersecurity. University professors can also use this book as a case study for senior seminars or graduate courses.

Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application, and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. This book discusses big data text-based mining to better understand the molecular architecture of diseases and to guide health care decision. It will be a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in understanding more about how to process and apply great amounts of data to improve their research.