Anomaly Detection in Machine Learning

Anomaly detection is a crucial process in machine learning that helps identify unusual patterns in datasets. It plays a vital role in multiple domains, ranging from fraud detection to system health monitoring. The process offers a way to automatically detect unusual and potentially critical events that may be hidden in large amounts of data.

Anomaly detection is a crucial technique in the field of machine learning. It involves identifying irregular patterns and outliers in data and understanding why they are different and what they signify. By sifting through large amounts of data, anomaly detection algorithms can flag irregularities that may indicate fraud, system failures, or unexpected opportunities. In this article, we will discuss the basics of anomaly detection in machine learning, exploring its importance, applications, and the underlying mechanisms that make it an essential tool in modern data analysis.

Table of Contents

• What are Anomalies? 
• How do you Recognize Anomalies?
• Purpose of Anomaly Detection
• Anomaly Detection in Machine Learning
• Machine Learning Algorithms for Anomaly Detection
• Applications on Anomaly detection
• Advantages of Anomaly Detection
• Limitations of Anomaly Detection
• Techniques for Anomaly Detection
• Anomaly Detection vs Outlier Detection
• How to Choose the Right Anomaly Detection Technique?
• Considerations For Anomaly Detection

What are Anomalies? 

An anomaly is a data point or suspicious event that contrasts with the baseline pattern. When data unexpectedly deviates from the established record, it can be an early sign of a system failure, security breach, or newly discovered security vulnerability. Anomalous data definitions include inconsistent or redundant data points in your database, such as incomplete data uploads, unexpected data deletions, and data insertion errors. 
Data anomalies don’t necessarily indicate a problem. Still, they are worth investigating to understand better why a discrepancy occurred and whether the anomaly is valid in your data set.

 

As shown in the diagram, the value increases exponentially with time, but there is a sudden increase in the value. This shows some anomalous behaviour.

Anomalies could be:

• Web application security anomalies
• Application performance anomalies
• Network anomaly

Also Read: How tech giants are using your data?

Also read: What is machine learning?

How Do You Recognize Anomalies?

• Statistical analysis: By calculating measures such as mean, median, and standard deviation, you can identify data points significantly different from the rest of the sample.
• Machine learning algorithms: Various machine learning algorithms can be used to identify anomalies, such as clustering, classification, and density-based methods.
• Data visualization: By creating charts or plots of your data, you can visually identify anomalies by looking for points or trends that stand out.
• Rule-based systems: You can set up rules or thresholds to flag data points that fall outside a specific range or violate certain conditions.
• Human inspection: In some cases, manually reviewing the data may be necessary to identify anomalies that are not easily detected through automated methods.

It is important to note that the specific method you use to detect anomalies will depend on the nature of your data and the goals of your analysis.

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Why We Need Anomaly Detection?

1. Product Performance: Combining anomaly detection with machine learning allows you to correlate and match existing data while maintaining generalization to get a complete picture of what is anomalous and find anomalous products.

2. Training performance: During the pre-training phase, anomaly detection can help by pointing out irregularities in the dataset that can cause model overfitting or misbehaviour.

3. Technical performance: A bug in the deployed system could expose the server to an active DDoS attack. Such errors can also be prevented proactively and addressed at the root by incorporating machine learning into your DevOps pipeline.

How to Detect Anomalies in Machine Learning?

Supervised Learning

For supervised anomaly detection, ML engineers need a training data set. Items in the dataset fall into two categories: normal and abnormal. The model uses these samples to extract patterns and detect previously unseen unusual patterns in the data.

 The quality of the training dataset is very important in supervised Learning. A lot of manual work is required as someone has to collect and label the samples. The most common supervised methods include Bayesian networks, k-nearest neighbours, decision trees, supervised neural networks, and SVM.

The advantage of supervised models is that they can provide higher detection rates than unsupervised methods. This is because the model output can return confidence scores, integrate both data and prior knowledge and encode dependencies between variables.

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Unsupervised Learning

For the unsupervised data set, no parts are marked as nominal or anomalous. Unsupervised settings require other tools to organize unstructured data. The main purpose of unstructured data is to create clusters from the data and find some groups that don’t belong. This type of anomaly detection is the most common type, and the best-known representative of unsupervised algorithms is the neural algorithm network.

Unsupervised settings require other tools to organize unstructured data. The main purpose of unstructured data is to create clusters from the data and find some groups that don’t belong.

Also read: Differences Between Supervised and Unsupervised Learning

Semi-supervised Learning

Semi-supervised anomaly detection works relatively simply and is exciting. Moreover, this mixture of supervised and unsupervised analogies had to be dealt with before. Typically, this happens when you have tagged input data but no identified outliers. The model learns trends in standard data from labelled training data and finds anomalies in unlabeled data above this threshold.

Machine Learning Algorithms for Anomaly Detection

K-means clustering

It is part of the unsupervised Learning of machine learning algorithms. This algorithm is used for unlabeled data (that is, data without defined categories, classes, or groups). This K-Means algorithm aims to find specific groups in the data. Where the variable K. represents the number of groups, This algorithm typically works iteratively to classify each data point by assigning it to one of K groups based on the characteristics of the data point. These data points are clustered based on feature similarity.

The K-Means clustering method helps form different clusters on average for each data point in a data set. You can usually find that the means of the objects in the cluster are the closest. Objects with thresholds greater than the nearest cluster mean are identified as outliers.

Support vector machine

The SVM model is a supervised learning model primarily used for classification. The ability to project data into alternative vector spaces to create sublevels has made ML an effective classification model. SVM uses only two anomaly detection classes and trains a model to maximize the difference or distance between her two datasets in the projected vector space. It also works with multiclass methods.

Anomalies are detected by examining points outside the range of categories. However, in the simplest case, one-class SVMs are widespread. Use SVM to determine if a data point belongs to a “normal” class – binary classification.

Isolation forests

Isolation Forest is an unsupervised anomaly detection algorithm that uses a random forest algorithm (decision trees) in the background to detect outliers in a data set. The algorithm attempts to slice or slice the data points to separate each observation from the other observations. Isolation forests are a type of decision tree algorithm that can be used to identify anomalies by randomly selecting features and creating partitions in the data. Similar to Random Forest, this algorithm randomly initializes the decision tree and continues branching nodes until all samples are left. Also, anomalies tend to be distinct from the rest of the data, so they are less likely to penetrate deep into the tree and grow faster than the rest of a given branch. The disadvantage of decision trees is that the final perception is very sensitive to how the data is partitioned at the nodes and is often biased. 

Benchmarking anomaly detection

There has yet to be a standard dataset for anomaly detection.

But according to the Github repo.

“NAB is a new benchmark for evaluating anomaly detection algorithms in real-time streaming applications. It consists of over 50 labeled real artificial time series data files and a new scoring mechanism designed for real-time applications.”

Now, what is NAB?

NAB is a standard open-source framework for evaluating real-time anomaly detection algorithms. Simply put, it is a repository that you can easily find on Kaggle. NAB consists of two main components: a dataset containing labelled real-world time series data and a scoring system designed for streaming data. The dataset contains 58 labelled files (approximately 365,000 data points) from various sources such as IT, industrial machine sensors, and social media.

Note: In the NAB benchmark, the best-performing anomaly detection algorithm to date captures 70% of anomalies from real-time data sets.

Applications on Anomaly Detection

1. Credit card fraud analysis using data mining technology

In today’s world, we are literally on the express train to a cashless society. According to the World Payments Report, total non-cash transactions in 2016 totalled 482.6 billion, up 10.1% from 2015. It’s huge! Cashless transactions are also expected to grow steadily in the coming years.

Even with EMV smart chips implemented, it is good to use anomaly detection for a huge number of fraudulent transactions.

So our data scientists are trying to find one of the best solutions for building models that predict fraudulent transactions.

2. Network intrusion detection

Anomaly detection can be used to identify unusual network traffic patterns, i.e. making intrusion detection systems that may indicate an attempted cyber-attack.

3. Medical diagnosis

Anomaly detection can be used to identify unusual patterns in patient data, such as vital signs or test results, that may indicate a medical condition.

4. Manufacturing quality control

Anomaly detection can be used to identify unusual patterns in production data that may indicate a problem with the manufacturing process.

5. Traffic prediction

Anomaly detection can identify unusual traffic data patterns that may indicate an issue with the transportation network, such as a road closure or accident.

6. Environmental monitoring

Anomaly detection can be used to identify unusual patterns in environmental data, such as temperature or air quality measurements, that may indicate a problem.

Advantages of Anomaly Detection

Anomaly detection is an essential technique for identifying patterns and outliers in data that deviate from the norm. It is used in many industries, including finance, healthcare, and manufacturing, to help identify potential fraud, equipment malfunctions, and other issues that may otherwise go unnoticed. 

Anomaly detection has several advantages, including:

• Early Detection: Anomaly detection can identify unusual patterns or observations in data at an early stage, allowing for early intervention and prevention of potential issues.

• Automation: Anomaly detection can be automated, allowing for continuous data monitoring and reducing the need for manual intervention.

• Scalability: Anomaly detection can be applied to large datasets, making it suitable for big data applications.

• Adaptability: Anomaly detection can be applied to various data types, including numerical data, time series data, and categorical data.

• Real-time Monitoring: Anomaly detection can be used for real-time data monitoring, allowing for immediate action in case of an anomaly.

Limitations of Anomaly Detection

Anomaly detection also has several limitations, including:

• Data Quality: Anomaly detection’s performance depends on the data’s quality. Poor quality data can result in false positives or false negatives.

• Choice of Algorithm: The choice of algorithm can also affect anomaly detection performance. Some algorithms may be better suited for certain types of data or specific use cases.

• The threshold for Determining Anomalies: The threshold for determining anomalies is subjective and can affect anomaly detection performance.

• The imbalance between Normal and Anomalous Data: As anomalous data is typically rare, it can be challenging to train a model that can accurately identify them. This is known as the class imbalance problem.

Techniques for Anomaly Detection

Several techniques can be used for anomaly detection, including:

• Clustering-based Anomaly Detection: This technique involves grouping similar data points into clusters and identifying data points that do not belong to any cluster as anomalies.

• Distance-based Anomaly Detection: This technique involves calculating the distance between a data point and the mean or median of the dataset and identifying data points farther away as anomalies.

• Probabilistic Anomaly Detection: This technique involves modelling the probability distribution of the data and identifying data points with a low probability of belonging to the distribution as anomalies.

• Rule-based Anomaly Detection: This technique involves defining rules based on domain knowledge to identify anomalies.

• Neural Network-based Anomaly Detection: This technique involves using neural networks, such as autoencoders, to learn a compact representation of the data and identify anomalies based on reconstruction errors.

Anomaly Detection vs Outlier Detection

Both terms are often used interchangeably, but they are different. Anomaly detection refers to identifying patterns or observations in data that do not conform to expected behaviour. In contrast, outlier detection refers to the process of identifying data points that are far away from the other data points.

How to Choose the Right Anomaly Detection Technique?

Choosing the proper anomaly detection technique depends on the specific use case and the characteristics of the data. Some factors to consider when choosing a technique include the following:

• Type of Data: Different techniques are better suited for different data types. For example, clustering-based techniques are well suited for categorical data, while distance-based techniques are well suited for numerical data.

• Size of the Data: Some techniques are better suited for big data applications, while others are more suitable for smaller datasets.

• Available Domain Knowledge: Some techniques, such as rule-based techniques, require domain knowledge to define the rules.

• Computational Resources: Some techniques, such as neural network-based techniques, require more computational resources than others.

Considerations For Anomaly Detection

There are several considerations to evaluate when implementing anomaly detection. One of the most important is selecting the appropriate algorithm for the specific use case. For example, supervised machine learning algorithms such as Random Forest or SVM may be more suitable for detecting fraud in financial transactions. 

In contrast, unsupervised approaches such as clustering or deep learning may be more appropriate for identifying equipment failures in a manufacturing setting.

Another important consideration is the data quality for training and testing the anomaly detection model. It’s essential to have a good representative, clean and labelled dataset. Anomaly detection models trained on poor-quality data will be less accurate and unreliable.

Additionally, it’s essential to clearly understand the business context and goals of the anomaly detection project. This will help ensure that the model is calibrated correctly, that the results are actionable, and provide value to the organisation.

Conclusion

Anomaly detection algorithms benefit fraud detection or disease detection case studies where the target class distribution is highly imbalanced. Anomaly detection algorithms are also designed to improve model performance by removing anomalies from training samples.