Understanding Deep Learning Anomaly Detection
Anomaly detection is an active research field widely applied to many commercial and mission-critical applications, including healthcare, fraud detection, industrial predictive maintenance, and cybersecurity. It is a process of discovering outliers, abnormal patterns, and unusual observations that deviate from established normal behaviors and expected characteristics in a system, dataset, or environment.
Many anomaly detection applications require domain-specific knowledge to extract actionable insights in a timely manner for informed decision-making and risk mitigation. For example, early detection of equipment performance degradation prevents unplanned downtime, whereas early discovery of disease threats prevents a pandemic outbreak.
The advent of cloud technologies, unlimited digital storage capacity, and a plethora of data have motivated deep learning research for anomaly detection. Detecting outliers requires an enormous dataset because anomalies are rare by nature in the presence of abundance. For example, detecting abnormal machinery vibrations and unusual power consumption or temperature increases allows companies to plan for predictive maintenance and avoid expensive downtime.
Deep learning anomaly detection has shown promising results in addressing challenges with the rare nature of anomalies, complex modeling of high-dimensional data, and identifying novel anomalous classes. The primary interest in anomaly detection is often focused on isolating undesirable data instances, such as product defects and safety risks, from the targeted domain. Other interests include improving model performance by removing noisy data or irrelevant outliers and identifying emerging trends from the dataset for a competitive advantage.
This chapter covers an overview of anomaly detection with the following topics:
- Exploring types of anomalies
- Discovering real-world use cases
- Considering when to use deep learning and what for
- Understanding challenges and opportunities
By the end of this chapter, you will have an understanding of the basics of anomaly detection, including real-world use cases, and the role of deep learning in accelerating outlier discovery. You will also have gained a sense of existing challenges and growth potential in leveraging deep learning techniques for anomaly detection.