​This book discusses cybersecurity issues of smart grid cyber-physical systems, focusing on the detection techniques against false data injection attacks. The authors discuss passive and proactive techniques that combat and mitigate two categories of false data injection attacks, false measurement data injections and false command data injections in smart grid cyber-physical systems. These techniques are easy to follow for either professionals or beginners. With this book, readers can quickly get an overview of this topic and get ideas of new solutions for false data injections in smart grid cyber-physical systems. Readers include researchers, academics, students, and professionals.

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

​This book discusses cybersecurity issues of smart grid cyber-physical systems, focusing on the detection techniques against false data injection attacks. The authors discuss passive and proactive techniques that combat and mitigate two categories of false data injection attacks, false measurement data injections and false command data injections in smart grid cyber-physical systems. These techniques are easy to follow for either professionals or beginners. With this book, readers can quickly get an overview of this topic and get ideas of new solutions for false data injections in smart grid cyber-physical systems. Readers include researchers, academics, students, and professionals.

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.