How Machine Learning Can Enable Anomaly Detection (2023)

As human, our brains are always tuned to spotting something out of the “normal” or the “usual stuff.” In short, some anomaly that does not fit with the usual pattern. With the abundant growth of data, data science tools are also looking for anomalies that do not subscribe to the normal data flow. For example, an “unusually high” number of login attempts may point to a potential cyberattack, or a major hike in credit card transactions in a short period could potentially be a credit card fraud.

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At the same time, detecting anomalies in the face of a continuous stream of unstructured data from various sources has its own challenges. An example of a challenge is to assume that a majority of credit card transactions are legitimate and proper while looking for major deviations in a few transactions that fall outside the “normal” range.

How Machine Learning Can Enable Anomaly Detection (1)

Thanks to the growth of various deep learning technologies, anomaly detection using machine learning (or ML) is a practical solution today. Machine learning algorithms can be deployed to define data patterns that are normal and using ML models to find deviations or anomalies.

(Video) A review of machine learning techniques for anomaly detection - Dr David Green

So, as a data analyst, how can you implement anomaly detection using machine learning? And what are the methods and benefits of anomaly detection using deep learning technologies? Let’s answer each of these questions and more in the following sections.

Also referred to as outlier detection, anomaly detection is simply the mode of detecting and identifying anomalous data in any data-based event or observation that differs majorly from the rest of the data. Anomalous data can be critical in detecting a rare data pattern or potential problem in the form of financial frauds, medical conditions, or even malfunctioning equipment.

How Machine Learning Can Enable Anomaly Detection (2)

How do you go about detecting an anomaly in data? Let’s examine this with the aid of an anomaly detection use case using 2 variables (X & Y). Consider the following visualized data that plots the X and Y variables.

How Machine Learning Can Enable Anomaly Detection (3)

Consider the data patterns of the 2 variables based on the plotted graphs to the right. Based on these data points, it’s not possible to detect any anomaly (or outlier). However, when the 2 variables are plotted against each other (as shown in the left figure), we can clearly detect the anomaly.

(Video) Anomaly Detection with Machine Learning

Does this bring us to the question as to why machine learning is required in anomaly detection? Detecting anomalies can be very challenging if you are plotting not two but hundreds of such variables in real-life scenarios.

How does machine learning help in outlier analysis? Let’s discuss this in the next section.

An outlier is identified as any data object or point that significantly deviates from the remaining data points. In data mining, outliers are commonly discarded as an exception or simply noise. However, the same cannot be done in anomaly detection, hence the emphasis on outlier analysis.

An example of performing anomaly detection using machine learning is the K-means clustering method. This method is used to detect the outlier based on their plotted distance from the closest cluster.

K-means clustering method involves the formation of multiple clusters of data points each with a mean value. Objects within a cluster have the closest mean value. Any object with a threshold value greater than the nearest cluster mean value is identified as an outlier. Here is the step-by-step method used in K-means clustering:

  1. Calculate the mean value of each cluster.
  2. Set an initial threshold value.
  3. During the testing process, determine the distance of each data point from the mean value.
  4. Identify the cluster that is nearest to the test data point.
  5. If the “Distance” value is more than the “threshold” value, then mark it as an outlier.

Next, let’s look at some of the other methods of executing anomaly detection using machine learning.

(Video) Lecture 15.1 — Anomaly Detection Problem | Motivation — [ Machine Learning | Andrew Ng ]

Based on different machine learning algorithms, anomaly detection methods are primarily classified under the following two headings

Supervised methods.

As the name suggests, this anomaly detection method requires the existence of a labeled dataset that contains both normal and anomalous data points. Examples of supervised methods include anomaly detection using neural networks, Bayesian networks, and the K-nearest neighbors (or k-NN) method.

Supervised methods provide a better rate of anomaly detection thanks to their ability to encode any interdependency between variables and including previous data in any predictive model.

Unsupervised methods

Unsupervised methods of anomaly detection do not depend on any training data with manual labeling. These methods are based on the statistical assumption that most of the inflowing data are normal and only a minor percentage would be anomalous data. These methods also estimate that any malicious data would be different statistically from normal data. Some of the unsupervised methods include the K-means method, autoencoders, and hypothesis-based analysis.

In the next sections, we shall look at some of the business benefits of anomaly detection using machine learning.

Using the capability of machine learning, anomaly detection has practical applications and benefits in different areas of business operations. Some of the benefits of the anomaly detection medium include:

Intrusion detection

Any nefarious activity that can damage an information system can be broadly classified as an intrusion. Anomaly detection can be effective in both detecting and solving intrusions of any kind. Common data-centric intrusions include cyberattacks, data breaches, or even data defects.

(Video) Machine Learning and Anomaly Detection

Mobile sensor data

Another benefit of anomaly detection using machine learning is in the domain of gathering and analyzing mobile sensor data. The growing adoption of IoT devices and the reduced costs of data capture through mobile sensors is definitely driving this trend.

For instance, a particular industry case study is that of the IBM Data Science Experience that developed a tool for anomaly detection using Jupyter Notebook for capturing sensor data from mobile phones and connected IoT devices.

Network server or app failure

Be it a mobile app or a network failure, a sudden degradation in performance can affect any business. Want to detect a sudden rise in the number of failed server requests? Anomaly detection code in Python programming can be used to detect any failing server on your network.

Additionally, anomaly detection can provide you with any supportive data that can identify the root cause of the problem.

Statistical Process Control

Statistical Process Control (or SPC) is a quality standard that is common in the manufacturing process. Quality-related data on product or process measure is retrieved during the manufacturing run-time process and is plotted on a graph to monitor if the data is within the configured control limits.

Anomaly detection is deployed to check if any data falls outside the control limits and to determine the root cause. In short, anomaly detection in SPC can be used to detect any product variation or any issue occurring in the manufacturing process that needs to be immediately resolved.

Future advancement in machine learning and deep learning technologies will only add to the scope of anomaly detection techniques and its value to business data. The increasing volume and complexity of data translate to major opportunities in harnessing this information for business success.

(Video) AWS Cloudwatch Alarms using Machine Learning (Anomaly Detection) | Overview

Since its inception, Countants has mastered deep learning solutions in artificial intelligence and machine learning for its global customers. If you have invested in machine learning tools like Python or Jupyter Notebook, then we can help you build business leverage from anomaly detection methods. Visit us at our website or call us now with your data-related queries.

FAQs

How is machine learning used in anomaly? ›

So when it comes to anomaly detection, kNN works as an unsupervised learning algorithm. A machine learning expert defines a range of normal and abnormal values manually, and the algorithm breaks this representation into classes by itself.

How do I enable anomaly detection? ›

Enable and Configure Anomaly Detection
  1. In Alert & Respond > Anomaly Detection, choose the desired application from the dropdown, and toggle Anomaly Detection ON. ...
  2. Select Alert & Respond > Anomaly Detection > Model Training to view Business Transaction training status.

What algorithm is used for anomaly detection? ›

Isolation Forest is an unsupervised anomaly detection algorithm that uses a random forest algorithm (decision trees) under the hood to detect outliers in the dataset. The algorithm tries to split or divide the data points such that each observation gets isolated from the others.

What are the three 3 basic approaches to anomaly detection? ›

There are three main classes of anomaly detection techniques: unsupervised, semi-supervised, and supervised. Essentially, the correct anomaly detection method depends on the available labels in the dataset.

Which machine learning technique is used to detect outliers? ›

Code for Outlier Detection Using Interquartile Range (IQR)

You can use the box plot, or the box and whisker plot, to explore the dataset and visualize the presence of outliers. The points that lie beyond the whiskers are detected as outliers. You can generate box plots in Seaborn using the boxplot function.

Why anomaly detection is unsupervised learning? ›

The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%.

Is anomaly detection deep learning? ›

Deep learning methods for anomaly detection can be complex, leading to their reputation as black box models. However, interpretability techniques such as LIME (see our previous report, “Interpretability”) and Deep SHAP provide opportunities for analysts to inspect their behavior and make them more interpretable.

Which functions work with anomaly detection? ›

Functions for Anomaly Detection. You can use simple functions, prediction-based functions, or statistical functions to examine trends that might indicate an anomaly. Simple functions can give insight into the rate of change and trends.

Is SVM used for anomaly detection? ›

In this article, I wanted to give a gentle introduction to One-Class SVM — a machine learning algorithm used for fraud/outlier/anomaly detection.

Which machine learning algorithm is used for object detection? ›

Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once). The R-CNN's are in the R-CNN family, while YOLO is part of the single-shot detector family.

What is anomaly detection in AI? ›

Anomaly detection is a technique that uses AI to identify abnormal behavior as compared to an established pattern. Anything that deviates from an established baseline pattern is considered an anomaly. Dynatrace's AI autogenerates baseline, detects anomalies, remediates root cause, and sends alerts.

Is anomaly detection classification or regression? ›

As you might see by now, supervised anomaly detection is actually classification, but overall they are two distinct machine learning problems. The two key factors for differentiating them are if you have labeled classes and whether it is an imbalanced dataset or not.

What type of analytics is anomaly detection? ›

About Anomaly Detection

Analytics Intelligence Anomaly Detection is a statistical technique to identify “outliers” in time-series data for a given dimension value or metric. First, Intelligence selects a period of historic data to train its forecasting model.

What are the data types of anomaly detection? ›

Generally speaking, anomalies in your business data fall into three main categories — global outliers, contextual outliers, and collective outliers.
  • Global outliers. Also known as point anomalies, these outliers exist far outside the entirety of a data set.
  • Contextual outliers. ...
  • Collective outliers.

What are the applications of anomaly detection? ›

Applications of anomaly detection include fraud detection in financial transactions, fault detection in manufacturing, intrusion detection in a computer network, monitoring sensor readings in an aircraft, spotting potential risk or medical problems in health data, and predictive maintenance.

What are the two main methods to detect outliers? ›

The two main types of outlier detection methods are: Using distance and density of data points for outlier detection. Building a model to predict data point distribution and highlighting outliers which don't meet a user-defined threshold.

Which is better for anomaly detection supervised or unsupervised? ›

We conclude that unsupervised methods are more powerful for anomaly detection in images, especially in a setting where only a small amount of anomalous data is available, or the data is unlabeled.

Can clustering be used for anomaly detection? ›

The main idea behind using clustering for anomaly detection is to learn the normal mode(s) in the data already available (train) and then using this information to point out if one point is anomalous or not when new data is provided (test).

Is anomaly detection descriptive or predictive? ›

For predictive maintenance of machines, anomaly detection tasks are the most relevant. Examples of mathematical concepts for unsupervised learning include PCA (Principal component analysis), SOM (Self organizing maps), Neural Networks, k-means clustering etc.

Which machine learning technique can be used for anomaly detection ai900? ›

Computer vision. Machine Learning (Regression)

What is an advantage of the anomaly detection method? ›

The benefits of anomaly detection include the ability to: Monitor any data source, including user logs, devices, networks, and servers. Rapidly identify zero-day attacks as well as unknown security threats. Find unusual behaviors across data sources that are not identified when using traditional security methods.

What are 3 things that can be anomalies? ›

Anomalies can be classified into the following three categories:
  • Point Anomalies. If one object can be observed against other objects as anomaly, it is a point anomaly. ...
  • Contextual Anomalies. If object is anomalous in some defined context. ...
  • Collective Anomalies.
10 Apr 2018

What is SVM used for in machine learning? ›

SVMs are used in applications like handwriting recognition, intrusion detection, face detection, email classification, gene classification, and in web pages. This is one of the reasons we use SVMs in machine learning. It can handle both classification and regression on linear and non-linear data.

Can random forest be used for anomaly detection? ›

Fraud detection techniques mostly stem from the anomaly detection branch of data science. If the dataset has sufficient number of fraud examples, supervised machine learning algorithms for classification like random forest, logistic regression can be used for fraud detection.

Can K means be used for anomaly detection? ›

In this demo, K-Means Clustering algorithm is used for anomaly detection. The model is developed in Python using TensorFlow.

What is machine learning in object detection? ›

Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. When humans look at images or video, we can recognize and locate objects of interest within a matter of moments. The goal of object detection is to replicate this intelligence using a computer.

Which machine learning algorithm is best for face recognition? ›

The most common type of machine learning algorithm used for facial recognition is a deep learning Convolutional Neural Network (CNN). CNNs are a type of artificial neural network that are well-suited for image classification tasks.

Why is CNN best for object detection? ›

R-CNN helps in localising objects with a deep network and training a high-capacity model with only a small quantity of annotated detection data. It achieves excellent object detection accuracy by using a deep ConvNet to classify object proposals.

What are the features of anomaly detection? ›

Anomaly Detector only takes in time series data by using timestamps and numbers. The service has no knowledge of the context and surroundings where the data is collected. In production use, decision makers might need to consider knowledge beyond those measures.

What are the characteristics of anomaly-based detection? ›

Anomaly-based IDSes typically work by taking a baseline of the normal traffic and activity taking place on the network. They can measure the present state of traffic on the network against this baseline in order to detect patterns that are not present in the traffic normally.

Which algorithm is best for outliers? ›

Isolation Forest Algorithm

Isolation forest is a tree-based algorithm that is very effective for both outlier and novelty detection in high-dimensional data.

What are the difficulties in anomaly detection? ›

Challenges in anomaly detection include appropriate feature extraction, defining normal behaviors, handling imbalanced distribution of normal and abnormal data, addressing the variations in abnormal behavior, sparse occurrence of abnormal events, environmental variations, camera movements, etc.

Which machine learning technique can be used for anomaly detection ai900? ›

Computer vision. Machine Learning (Regression)

Is anomaly detection deep learning? ›

Deep learning methods for anomaly detection can be complex, leading to their reputation as black box models. However, interpretability techniques such as LIME (see our previous report, “Interpretability”) and Deep SHAP provide opportunities for analysts to inspect their behavior and make them more interpretable.

What is anomaly detection in AI? ›

Anomaly detection is a technique that uses AI to identify abnormal behavior as compared to an established pattern. Anything that deviates from an established baseline pattern is considered an anomaly. Dynatrace's AI autogenerates baseline, detects anomalies, remediates root cause, and sends alerts.

Is SVM used for anomaly detection? ›

In this article, I wanted to give a gentle introduction to One-Class SVM — a machine learning algorithm used for fraud/outlier/anomaly detection.

Which machine learning algorithm is used for object detection? ›

Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once). The R-CNN's are in the R-CNN family, while YOLO is part of the single-shot detector family.

Is anomaly detection supervised or unsupervised? ›

We conclude that unsupervised methods are more powerful for anomaly detection in images, especially in a setting where only a small amount of anomalous data is available, or the data is unlabeled.

Which machine learning algorithm is best for image recognition? ›

Convolutional Neural Networks (CNNs) is the most popular neural network model being used for image classification problem.

What type of analytics is anomaly detection? ›

About Anomaly Detection

Analytics Intelligence Anomaly Detection is a statistical technique to identify “outliers” in time-series data for a given dimension value or metric. First, Intelligence selects a period of historic data to train its forecasting model.

Is anomaly detection classification or regression? ›

As you might see by now, supervised anomaly detection is actually classification, but overall they are two distinct machine learning problems. The two key factors for differentiating them are if you have labeled classes and whether it is an imbalanced dataset or not.

Which functions work with anomaly detection? ›

Functions for Anomaly Detection. You can use simple functions, prediction-based functions, or statistical functions to examine trends that might indicate an anomaly. Simple functions can give insight into the rate of change and trends.

What are the applications of anomaly detection? ›

Applications of anomaly detection include fraud detection in financial transactions, fault detection in manufacturing, intrusion detection in a computer network, monitoring sensor readings in an aircraft, spotting potential risk or medical problems in health data, and predictive maintenance.

What are 3 things that can be anomalies? ›

Anomalies can be classified into the following three categories:
  • Point Anomalies. If one object can be observed against other objects as anomaly, it is a point anomaly. ...
  • Contextual Anomalies. If object is anomalous in some defined context. ...
  • Collective Anomalies.
10 Apr 2018

Can clustering be used for anomaly detection? ›

The main idea behind using clustering for anomaly detection is to learn the normal mode(s) in the data already available (train) and then using this information to point out if one point is anomalous or not when new data is provided (test).

What is SVM used for in machine learning? ›

SVMs are used in applications like handwriting recognition, intrusion detection, face detection, email classification, gene classification, and in web pages. This is one of the reasons we use SVMs in machine learning. It can handle both classification and regression on linear and non-linear data.

Can random forest be used for anomaly detection? ›

Fraud detection techniques mostly stem from the anomaly detection branch of data science. If the dataset has sufficient number of fraud examples, supervised machine learning algorithms for classification like random forest, logistic regression can be used for fraud detection.

Videos

1. Anomaly Detection Based on Log Analysis | AI/ML IN 5G CHALLENGE
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4. Machine Learning Anomaly Detection with Python and Power BI
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5. Anomaly Detection in Cellular IoT with Machine Learning
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