  An outlier is an event or observation that is defined as an unusual activity,
intrusion, or a suspicious data point that lies at an irregular distance from a
population. The definition of an outlier event, however, is subjective and
depends on the application and the domain (Energy, Health, Wireless Network,
etc.). It is important to detect outlier events as carefully as possible to
avoid infrastructure failures because anomalous events can cause minor to
severe damage to infrastructure. For instance, an attack on a cyber-physical
system such as a microgrid may initiate voltage or frequency instability,
thereby damaging a smart inverter which involves very expensive repairing.
Unusual activities in microgrids can be mechanical faults, behavior changes in
the system, human or instrument errors or a malicious attack. Accordingly, and
due to its variability, Outlier Detection (OD) is an ever-growing research
field. In this chapter, we discuss the progress of OD methods using AI
techniques. For that, the fundamental concepts of each OD model are introduced
via multiple categories. Broad range of OD methods are categorized into six
major categories: Statistical-based, Distance-based, Density-based,
Clustering-based, Learning-based, and Ensemble methods. For every category, we
discuss recent state-of-the-art approaches, their application areas, and
performances. After that, a brief discussion regarding the advantages,
disadvantages, and challenges of each technique is provided with
recommendations on future research directions. This survey aims to guide the
reader to better understand recent progress of OD methods for the assurance of
AI.
