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Anomaly detection is the process of finding patterns in data that don’t conform to a model of normal behavior. Unless you’re a data scientist or practitioner familiar with tools that offer algorithms for pattern recognition, the principles behind anomaly detection may seem obscure and unapproachable. But the benefits are clear.

The goal of anomaly detection is to identify cases that are unusual within data that is seemingly comparable. Anomaly detection is an important tool for detecting fraud, network intrusion, and other rare events that may have great significance but are hard to find.

Anomaly detection can be used effectively as a tool for risk mitigation and fraud detection. Eric Ogren, the senior security analyst at 451 Research, describes anomaly detection as “security analytics.” However, this security solution is more than detecting something out of normal, As Ogren says, “It’s more of, ‘How do you determine if an outlier activity is a security issue?’”

Today, data drives most business decisions. With access to more data, more information than ever before, it is even more important to analyze it and interpret it correctly. When it comes to security, finding the outliers is only the first step. Determining if the outlier is a security threat, and understanding the root cause of the anomaly is the key for a real solution. Quoting Ogren again, “Two years from now, analytics will drive most organizations’ security strategies as operations teams use insights gleaned from analytics to apply preventive measures. It will be analytics first, and then more pinpoint, siloed-type approaches based on what the analytics tell you.”

That’s where analytics comes in, as it can help us to learn about the behaviors of the application or system or database or a combination of those things from the historical data, by being able to identify anomalies or trends before they are othwerise apparent. Understanding such behaviors proactively enables one to proactively find trends and see things that are going on before they become problems.

How Anomaly Detection Works

Anomaly detection techniques rely on Machine Learning. Machine learning can be used to learn the characteristics of a system from observed data, helping to enhance the speed of detection. Machine-learning algorithms not only learn from the data, but they’re also able to make predictions based on that data, also to improve their predictive abilities by “learning” from the results fo their initial predictions as the events actually play out in real life (the feedback loop). Machine learning for anomaly detection includes techniques that enable you to effectively detect and classify anomalies in large and complex big data sets. Anomaly-detection methods include sequential hypothesis tests, such as cumulative sum charts and sequential probability ratio tests, for detecting changes in the distributions of real-time data and setting alert parameters.

Anomaly Detection Use Cases

Industries which benefit greatly from anomaly detection include:

  • Banking, Financial Services, and Insurance (BFSI) – In the banking sector, some of the use cases for anomaly detection are to flag abnormally high transactions, fraudulent activity, and phishing attacks.
  • Retail – In Retail, anomaly detection is used for processing large volumes of financial transactions to identify fraudulent behaviors, such as identity theft and fraudulent credit card usage.
  • Manufacturing – In Manufacturing, anomaly detection can be used in a number of important ways, such as identifying machines and tools that are underperforming, which can take months to find without anomaly detection technology.
  • IT and Telecom – In IT and Telecommunications, anomaly detection is increasingly valuable to detect and act on personal threats to users, financial threats to service providers, or other types of unexpected threats.
  • Defense and Government – In Defense and Government setting, anomaly detection is best used for identifying excessive and fraudulent government spending, budgeting, and audits. This can save governments an immense amount of money.
  • Healthcare – In Health Care, anomaly detection is used for its application in a crucial management task that can improve the quality of the health services and avoid loss of huge amounts of money. In terms of identifying fraudulent claims from hospitals and on the side of the insurance providers.

Sources/More Information:

Alla S., Adari S.K. (2019) Practical Use Cases of Anomaly Detection. In: Beginning Anomaly Detection Using Python-Based Deep Learning. Apress, Berkeley, CA.

Campos, M.M., Milenova, B.L., Yarmus, J.S., “Creation and Deployment of Data Mining-Based Intrusion Detection Systems in Oracle Database 10g”


Rosencrance, L. (2019, January 22). What IT Ops needs to know about anomaly detection: Better security and ops. Retrieved August 17, 2020, from

Chaparro, C., & Eberle, W. (2018). Detecting Anomalies in Mobile Telecommunication Networks Using a Graph Based Approach [Abstract]. Detecting Anomalies in Mobile Telecommunication Networks Using a Graph Based Approach.