The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. If not addressed properly, such attacks will not only cripple SCC operations but also influence the extent to which customers are willing to share data.

Anomaly detection, prognostication and automated mitigation are very critical for data center management. Most of these approaches can be divided into two categories - model-based and data-driven. While model-based techniques rely on physics guided models that can explain and predict the expected progression of parameters such as temperature and voltage in electronics, the data-driven approach is suitable for complex scenarios where a suitable physics based model is unavailable. The data-driven approaches can be further divided into supervised and unsupervised methods.

Cyber-physical systems (CPS) are engineered systems created as networks of interacting physical and computational processes. Most modern products in major industrial sectors, such as automotive, avionics, medical devices, and power systems already are or rapidly becoming CPS driven by new requirements and competitive pressures.