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Anomaly Detection in Machine Learning: ML Algorithms For Anomaly Detection

Anomaly detection in Machine Learning is identifying outliers to prevent network intrusions, adversary attacks, frauds, etc.

Every organization must focus on in-house business operations to ensure everything works efficiently.

However, a sudden suspicious activity due to data corruption or human error can impact a model’s performance. This is where anomaly detection in ML comes into the picture.

The approach identifies outlier data points to make the entire dataset free of anomalies. In this article, we’ve touched on every aspect of anomaly detection. Let’s get started!

ALSO READ: AWS Community Day 2022 With AWS Experts For AWS Insights

What Is Anomaly Detection in Machine Learning?

Anomalies are data points that do not confirm normal data behavior amongst other data points in the dataset. There are three board categories of anomaly detection.

  • Point Anomaly – if the tuple is far from the rest of the data. 
  • Contextual Anomaly – if the anomaly is due to the context of a specific observation. 
  • Collective Anomaly – the data set that helps you to find an abnormality.

The process of catching these outliers/anomalies is called anomaly detection. It is done using the concept of Machine Learning.

Anomaly Detection in Machine Learning

ML Algorithms For Anomaly Detection 

Multiple Machine Learning algorithms are available that will help you with anomaly detection. Below is the list of top ML and data science algorithms that every data scientist needs to understand.


DBSCAN uncovers clusters in large spatial datasets based on the principle of density. When used for anomaly detection, the algorithm handles outliers where non-discrete data points represent data.

  • K-Nearest Neighbors

K-nearest neighbors have supervised Machine Learning algorithms used for classification. It is a valuable tool that helps to visualize data points and make anomaly detection intuitive.

  • Support Vector Machines 

Another supervised Machine Learning algorithm is SVM which uses hyperplanes in multi-dimensional space. SVM is effective when more than one class is involved in the problem.

  • Bayesian Networks

Bayesian networks enable Machine Learning engineers to discover defects in high-dimensional data. When anomalies are subtle and more complex, ML engineers use bayesian networks to discover and visualize abnormalities.

Different algorithms are used for anomaly detection in Machine Learning to get desired results. However, before commencing the project, you should have an experienced ML engineer team.

Need For Machine Learning In Anomaly Detection

According to Anomaly Detection Solution Market Research Report, the worldwide anomaly detection solution is expected to grow at a booming CAGR between 2022-2030. Let us now understand a few ways to use anomaly detection in Machine Learning in the real world.

  • Intrusion Detection 

Companies are concerned about protecting confidential data about clients and employees. Intrusion detection systems detect malicious activity and notify the team of actions.

  • Defect Detection

The manufacturing industry especially has to deal with defects in the supply chain. Anomaly detection in Machine Learning uses computer vision to monitor internal systems and detect faults.

  • Fraud Detection

Like intrusion detection, fraud detection aims to prevent activities that focus on obtaining money or property from customers. In addition, anomaly detection in ML detects fraudulent activities and documents to protect business operations.

  • Health Monitoring

Anomaly detection systems are incredibly useful in the healthcare sector. They assist health professionals in detecting unusual patterns in test results and MRIs to give a more accurate diagnosis.

Inferenz has worked with a manufacturing and eCommerce company to help them with text mining solutions. The AI and ML algorithms helped the company with 100% information availability and improved data quality. 

Different Anomaly Detection Methods

There are three different types of anomaly detection methods with Machine Learning.

  • Supervised – This method labels the datasets as normal and abnormal samples to classify future data points. ML engineers must collect and label examples to train the datasets in supervised anomaly detection. 
  • Unsupervised – It is the most common type of anomaly detection in Machine Learning that does not involve manual work. Artificial neural networks can be applied to unstructured data to determine anomalies. 
  • Semi-Supervised – This anomaly detection method combines supervised and unsupervised methods. ML engineers can automate anomaly detection of unstructured data with unsupervised learning methods and monitor the process to improve accuracy.

Anomaly Detection in Machine Learning

Make Your Business Smart With ML Solutions 

Machine Learning and computer vision are the leading technologies used by anomaly detection systems. Adapting this latest technology like ML will help you automate anomaly detection and efficiently manage large datasets.

If you intend to scale your business operations, automate redundant tasks, and digitize your business, contact Inferenz ML experts. The team helps companies to implement systems like anomaly detection in Machine Learning to manage their large datasets better.

FAQs for Anamoly Detection in ML

  • Which algorithm will you use for anomaly detection?

Data scientists can use multiple Machine Learning algorithms for anomaly detection depending on data type and size. Some standard algorithms include Local outlier factor (LOF), K-nearest neighbors, support vector machines, and more.

  • What are the three basic approaches to anomaly detection in ML?

The three basic approaches to anomaly detection in ML are unsupervised, semi-supervised, and supervised.

  • What is anomaly detection in AI?

Anomaly detection uses Machine Learning and Artificial Intelligence to identify abnormal behavior in datasets by comparing it with an established pattern.

  • How does anomaly detection work?

Anomaly detection works by analyzing the business data to identify the data points that do not align with standard data patterns. It helps to investigate inconsistent data, identify deviations from baseline, and so on.