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What are the application of anomaly detection?
In anomaly detection, the objective is to discover objects that are different from multiple objects. Often, anomalous objects are referred to as outliers, because on a scatter plot of the data, they lie far away from multiple data points. Anomaly detection is called a deviation detection, because anomalous objects have attribute values that deviate essentially from the expected or general attribute values, or as exception mining, because anomalies are exceptional in several sense.
There are various application of anomalies detection which are as follows −
Fraud Detection − The buying behavior of someone who keep a credit card is different from that of the initial owner. Credit card companies tries to identify a theft by viewing for buying designs that characterize theft or by perceiving a change from general behavior. Same methods are used for different types of fraud.
Intrusion Detection − Unfortunately, attacks on computer systems and computer networks are customary. While several attacks, including those designed to disable or overwhelm computers and networks, are obvious, other attacks, including those designed to secretly gather data, are complex to identify. Some of these intrusions can be identified by observing systems and networks for unusual behavior.
Ecosystem Disturbances − In the common world, there are general events that can have an essential effect on human beings. Examples contains hurricanes, floods, droughts, heat waves, and fires. The objective is to forecast the likelihood of these events and the causes of them.
Public Health − In some countries, hospitals and medical clinics report several statistics to national organizations for more analysis. For instance, if some children in a city are vaccinated for a specific disease, such as measles, then the appearance of a some cases scattered across several hospitals in a city is an anomalous event that can denote a problem with the vaccination programs in the city.
Medicine − For a specific patient, unusual symptoms or test results can denote potential health issues. However, whether a specific test result is anomalous can based on multiple characteristics of the patient, including age and sex. Moreover, the categorization of a result as anomalous or not acquire a cost-unneeded more tests if a patient is active and possible harm to the patient if a condition is left undiagnosed and untreated.
Although some current interest in anomaly detection has been driven by software in which anomalies are the target, historically, anomaly detection has been considered as a technique for enhancing the analysis of general data objects.
For example, an associatively small number of outliers can alter the mean and standard deviation of a group of values or change the set of clusters created by a clustering algorithm. Hence, anomaly detection is an element of data preprocessing.
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