Unsupervised Contextual Anomaly Detection: What it means and how it works
If you've ever been to a bank, you may have seen an alarm go off if someone tries to rob it. That alarm is an example of supervised anomaly detection, where a system is taught what is normal and what is not. However, sometimes there are rare events or objects that the system has not seen before, and that's where unsupervised anomaly detection comes in. Unsupervised anomaly detection is like having a system that can detect if someone is doing something unusual, even if it has never seen that specific behavior before. Unsupervised contextual anomaly detection takes it a step further by looking at not just the behavior, but also the environment around it.
What is Unsupervised Contextual Anomaly Detection?
Unsupervised contextual anomaly detection is a way to detect unusual occurrences using two types of data – behavioral and contextual. Behavioral attributes are those directly related to the process being monitored, while contextual attributes are exogenous, or external, attributes that affect the behavior in some way. For example, if someone is trying to detect anomalies in a factory production line, the behavioral attributes might include the temperature of the machines and the number of items produced per hour. The contextual attributes might include the temperature of the environment or the time of day. The idea behind unsupervised contextual anomaly detection is that the behavioral attributes are often conditional on the contextual attributes. By looking at both types of data, the system can detect unusual occurrences that might not have been visible if only looking at behavioral data alone.
How does Unsupervised Contextual Anomaly Detection work?
Unsupervised contextual anomaly detection uses statistical models to identify anomalies. One popular model is called the "joint deep variational generative model," which is a type of machine learning model that can handle both behavioral and contextual data. The model works by creating a representation of what is normal, based on the data it has seen. This is called the "latent space," and it is a place where the model can represent abnormalities as deviations from what is normal. The model can then determine if new data falls within the latent space or if it is outside of it. If the data is outside of the latent space, it is identified as an anomaly.
One advantage of the joint deep variational generative model is that it can handle many different types of data, including images and text. For example, if someone is trying to detect anomalies in medical records, the behavioral attributes might include the patient's blood pressure and heart rate, while the contextual attributes might include the patient's age and gender. The model can take all of this data and create a representation of what is normal, and then identify when something is outside of that representation.
What are the applications of Unsupervised Contextual Anomaly Detection?
Unsupervised contextual anomaly detection has many applications across different industries. For example, it can be used in healthcare to detect unusual medical conditions, in financial services to detect fraudulent transactions, and in manufacturing to detect anomalies in production processes.
In healthcare, unsupervised contextual anomaly detection can be used to detect unusual patient symptoms or lab results. By looking at both the patient's medical history and external factors like age and gender, the system can detect when something is abnormal and alert medical professionals.
In financial services, unsupervised contextual anomaly detection can be used to detect fraudulent transactions. By looking at both the behavior of the account holder and external factors like location and time of day, the system can detect when something is unusual and flag the transaction as possibly fraudulent.
In manufacturing, unsupervised contextual anomaly detection can be used to detect anomalies in production processes. By looking at both the behavior of the machines and external factors like temperature and humidity, the system can detect when something is abnormal and alert maintenance teams.
Unsupervised contextual anomaly detection is a powerful tool for detecting unusual occurrences in different types of data. By looking at both behavioral and contextual attributes, the system can detect anomalies that might not be apparent if only looking at one type of data. As technology continues to evolve, unsupervised contextual anomaly detection will become an increasingly important tool for keeping people safe, protecting financial assets, and ensuring smooth production processes.
