Anomaly detection is the process of locating unusual points or patterns in a set of data. Anything that deviates from an established baseline (within a certain, predefined tolerance) is considered an anomaly. Detecting these anomalies is now a critical practice, as anomalies can be indicators of a security breach, a hardware or software problem, shifting customer demands, or any number of challenges that require immediate attention.
Anomalies aren’t always bad. If sales suddenly spike because a famous social media influencer has written about a company’s product, this anomalous behavior could be beneficial. But is the business prepared for this sales spike? What’s more important is that the organization needs to have a system in place to become aware of any anomalous behavior, good or bad, so that it may respond accordingly — whether that is patching a security flaw, replacing a failing component or deploying additional servers to keep up with rising sales. Anomaly detection — especially unsupervised anomaly detection, which identifies previously unseen rare events without prior knowledge — is also playing an increasingly important role in cybersecurity, particularly with the rise of zero trust methodologies that rely on constant network surveillance for bad actors.
The process of anomaly detection revolves around the use of statistical tools and other methodologies applied to metrics or a dataset. Machine learning techniques are also becoming increasingly important to help discover anomalies as datasets become very large and complex.
In this article, we’ll discuss the various types of anomalies and the benefits of detecting them, while investigating the process of anomaly detection in greater detail.