Searching for a needle in a haystack is an important task in several
contexts of data analysis and decision-making. Examples include
identifying the insider threat within an organization, the prediction of
failure in industrial production, or pinpointing the unique signature of
a solo perpetrator, such as a school shooter or a lone wolf terrorist.
It is a challenge different from that of identifying a rare event (e.g.,
a tsunami) or detecting anomalies because the "needle" is not easily
distinguished from the haystack. This challenging context is imbued with
particular difficulties, from the lack of sufficient data to train a
machine learning model through the identification of the relevant
features and up to the painful price of false alarms, which might cause
us to question the relevance of machine learning solutions even if they
perform well according to common performance criteria. In this book,
Prof. Neuman approaches the problem of finding the needle by
specifically focusing on the human factor, from solo perpetrators to
insider threats. Providing for the first time a deep, critical,
multidimensional, and methodological analysis of the challenge, the book
offers data scientists and decision makers a deep scientific
foundational approach combined with a pragmatic practical approach that
may guide them in searching for a needle in a haystack.
