A Systematic Review on Anomaly Detection

Abstract

Anomaly detection has been used for many years to perceive and extract anomalous points from data. This is an important question that has been explored in various research areas and application domains. Many anomaly detection techniques are specifically designed for specific application domains, while others are more general. Many data science strategies had been used to come across anomalies. One widely used technique is deep machine learning, which play an important role in this field. This research paper provides a systematic literature review analysing ML models for detecting anomalies. Our review analyses the models from four perspectives: the Problem nature and challenges, Classification and formulation, Review of past work, and the future opportunities. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. We also discuss the computational complexity of the technique, as this is an important issue in real application domains. We hope that this paper will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.