Anomaly detection, also popularly known as outlier detection, refers to the process of leveraging the power of artificial intelligence to identify unusual patterns or events in data. It is one of the most popular tools that can help users identify security threats, detect fraud, and maintain the overall performance of complex systems. But this is not it as there is a lot to learn about anomaly detection in AI. Let’s get started with some fun learning.
What is a Data Anomaly?
Before moving on, let us first brush our basics with understanding what is a data anomaly:
- There are certain patterns in every dataset which signify that everything is going normal and there are no disruptions.
- However, there might be times when users notice unexpected changes in data patterns. Similarly, there might be occurrences that do not conform to the adhered data pattern. This is what we call anomalies.
- Interestingly, anomalies are known by different names such as exceptions, peculiarities, deviations, and outliers, depending on a few essential factors.
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Understanding Anomaly Detection in AI
Anomaly detection is a process conducted in data mining that typically involves identifying unusual or abnormal outlier values in a particular dataset. For this, it assumes that the data one possesses must fall within a particular range which can be based on history. In addition to this, anomaly detection can be used on single variables which is known as Unvariate Anomaly Detection, or on a combination of variables known as Multivariate Anomaly Detection.
Why is Anomaly Detection Important?
Anomaly detection is important since it can help users identify and respond to unusual patterns in data. This can be an advantage in a wide range of applications such as:
- Reduce fraud losses: It can help identify fraudulent transactions to protect consumers from financial losses.
- Protection against cyberattacks: Anomaly detection can be used to identify and prevent users from cyberattacks.
- Monitoring performance: It can also help monitor the overall performance of your systems and mitigate the chances of outages.
- Early diagnoses: Medical diagnosis is another amazing benefit of anomaly detection. This way doctors can diagnose and treat life-threatening diseases in time.
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Assumptions in Anomaly Detection
Anomaly detection comes with two basic assumptions:
- Anomalies in the data occur rarely.
- The features of these anomalies differ from other usual instances significantly
Types of Anomaly Detection
Here are the types of anomaly detection along with their descriptions:
- Supervised anomaly detection: It uses labeled data to train and identify anomalies in data. For the unaware, labeled data simply means that each data point is classified as either normal or unusual. Once the model is trained about the characteristics of normal data, it can be used to identify points that fall out of the expected range.
- Unsupervised anomaly detection: This technique identifies unusual patterns without any prior knowledge of normal data. Unlike supervised anomaly detection, it does not assign label data to normal and unusual data.
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The most common use of anomaly detection in machine learning is to enhance the overall quality of datasets. It does so by first identifying outliers and then removing them efficiently. By removing outliers, the technique of anomaly detection can help users improve the accuracy of numerous machine learning models.
There are three types of anomaly detection techniques which are unsupervised, supervised, and semi-supervised.
Anomaly detection can help to reduce fraud, diagnose health issues, prevent cyberattacks, fight network intrusion, and do a lot more good.