Anomaly Detection

Anomaly detection can be termed as a technique, which is deployed to identify various unusual patterns, which are not in collation with the expected behavior of the data. These unnatural occurrences are also termed as outliners. The application of Anomaly detection starts with the involvement of the business intrusion aspect in business, where it identifies unnatural patterns within the network traffic, which can eventually signal a system hack.

Another field where Anomaly detection is deployed is the health monitoring which is based on a system. It can help with the function of detecting a malignant tumor through an MRI scan. Anomaly detection also helps in fraud detection in the banking sector, where it can prevent the occurrence of unwanted financial transactions.

Types of Anomalies

In order to understand the various techniques that help in Anomaly detection, it is vital to comprehend various prevalent anomalies:

  • Point Anomalies: These can be termed as a single or “on-off” event of a particular data interaction, which is very different from the routine transactions. The common example in banking is when an absurd amount spent is detected in a credit card statement, which helps in detecting fraud.
  • Contextual Anomalies: This particular abnormality can be specified through context. The very common occurrence of such anomaly is within the time-series data. For example; it is common to spend about $100 on food every day during holidays, but on normal days, it is considered irrational.
  • Collective Anomalies: The incident, when a particular “set” of data collectively helps in identifying anomalies is known as Collective Anomalies. In real-time, it can be defined; when someone tries to copy a series of data from the local host the remote machine being pre-defined, it can be flagged as a potential cyber-attack.

Anomaly Detection Techniques

Various techniques are deployed to identify anomalies in a given data environment. These techniques work in conjunction with the nature of the business and the nature of the given data.

A. Simple Statistical Methods

The basic and the simplest way to identify the irregularities in data occurrences is by flagging the abnormalities. The data points are flagged on the basis of their uncommon behavior, other than the pre-set patterns based on mean, median, mode, and quantiles.

Various Challenges Associated with the Method:

  • Sometimes there is “noise” present in the data, which is similar to the abnormalities, which makes it very difficult to identify the anomaly.
  • The definition associated with the abnormality can be changed frequently, and the adversities adopt themselves very quickly.
  • The data pattern is based on seasonality.

B. Machine Learning Based Approaches

Given below are various machine learning based techniques to identify anomalies:

i. Density Based Anomaly Detection

This method is based on the “k-nearest” neighbour algorithm. Using a score does the evaluation of the nearest set of data. The score can be Euclidian distance or a measure similar to it, which is dependent on the type of data, whether numerical or categorical.  

ii. Clustering-Based Anomaly Detection

In the domain of unsupervised learning, Clustering is considered as one of the most popular concepts. Clustering algorithm widely used the K-means. “K” is created in the similar cluster as the data points. Anomalies are identified as the data instances that fall outside of these defined groups.

iii. Support Vector Machine-Based Anomaly Detection

This is another very effective technique for detecting anomalies. It is associated with supervised learning and involves extensions such as OneClassCVM. The algorithm learns a soft boundry in order to cluster the data instances, which are “normal.” The instances that occur outside of the normal pattern are marked as anomalies.

Thus, anomaly detection is of great significance and finds application in various industries and sectors.