One of the most typical uses for machine learning is anomaly detection. Preventing fraud, adversary attacks, and network intrusions that could jeopardize the future of your business by locating and identifying outliers.
We will discuss how anomaly detection functions in this post, along with some helpful machine-learning techniques and other details. Keep reading!
Table of Contents
What Is Anomaly Detection?
Any process that identifies the outliers—those data points that don’t belong—in a dataset is an anomaly detection process. These anomalies may indicate unusual network activity, reveal a malfunctioning sensor, or simply indicate the need for data cleaning prior to analysis.
The performance of the system must be managed and monitored in the world of distributed systems we live in today, though it is a necessary chore. With hundreds or thousands of items to keep an eye on, anomaly detection can help identify the location of errors, enhancing root cause analysis and enabling quick access to technical support. By spotting anomalies and alerting the relevant parties to take action, anomaly detection aids the chaos engineering monitoring cause.
In enterprise IT, anomaly detection is commonly used for:
- Data cleaning
- Intrusion detection
- Fraud detection
- Systems health monitoring
- Event detection in sensor networks
- Ecosystem disturbances
Different Types Of Anomalies
Let’s now examine the different types of anomalies and outliers that machine learning engineers typically encounter.
Global Outliers
A data point can be considered a global anomaly if its value falls outside the bounds of all the other data points in the dataset. In other words, it’s a rare occurrence.
The analytics team at the bank would be alarmed if, for instance, you consistently deposit an average American salary into your bank accounts but one month receives a million dollars.
Contextual Outliers
When a contextual outlier is referred to, it means that its value doesn’t match what we would anticipate seeing for a comparable data point in the same context. The same circumstance observed in various contexts can occur because contexts are typically temporal and are not always abnormal.
For instance, it’s common for shops to see an increase in customers around the holidays. However, if a sudden uptick occurs outside of holidays or sales, it may be viewed as a contextual outlier.
Collective Outliers
A subset of data points that deviate from the average behavior is collectively referred to as outliers.
Tech businesses, in general, tend to expand continuously. Although it’s not a common occurrence, some businesses may decline. However, we can spot a collective outlier if a number of businesses simultaneously show a decline in revenue over the same time period.
Anomaly Detection In Machine Learning
The data-driven task of finding these exceptional occurrences and filtering or modulating them out of the analysis pipeline is known as anomaly (or outlier) detection. Such anomalous events may be related to a flaw in the data source, such as fraud in the financial sector, a problem with the equipment, or errors in time series analysis.
Such anomalies can be identified and reported either retroactively or immediately using machine learning models. These unusual data points can then either be removed to preserve the data’s integrity before further processing or flagged for business-related analysis.
The time-series data predictions and the actual occurrence are compared below. Until an anomaly happens, as can be seen, the forecast closely mirrors the actual data. The past data trend and the model prediction, both shown in blue, show that this significant variation is unexpected. From a much more complicated dataset, machine learning models can be trained to recognize these out-of-distribution anomalies.
Also Read: Stacking Machine Learning
Why Does Anomaly Detection Require Machine Learning?
Machine learning and statistical tools are frequently used in this process.
The majority of modern businesses that need outlier detection deal with vast amounts of data, including transactions, text, image, and video content, among other types of information. You would need days to complete all the transitions that take place inside a bank every hour, and more are created every second. It is simply not possible to manually extract any significant insights from this volume of data.
Another challenge is that the data is frequently unstructured, meaning that it wasn’t put in a particular order for the data analysis. Unstructured data includes things like business documents, emails, or images.
Use tools that aren’t afraid of large volumes of data in order to be able to gather, clean, structure, analyze, and store data. The best outcomes for machine learning techniques actually occur when big data sets are involved. Most types of data can be processed by machine learning algorithms. Additionally, you can select the algorithm based on your issue and even combine different methods for the best outcomes.
By saving resources and streamlining the anomaly detection process, machine learning is being used in practical applications. It can take place both post-factum and in the present. To increase security and robustness, real-time anomaly detection is used in areas like cybersecurity and fraud detection.
How Do Anomaly Detection Techniques Work?
Machine learning can be used to detect anomalies in a variety of ways.
Supervised
A training dataset is required by ML engineers for supervised anomaly detection. The dataset’s elements are divided into two groups: normal and abnormal. These illustrations will be used by the model to extract patterns from previously unobserved data and to identify abnormal patterns.
The caliber of the training dataset is crucial in supervised learning. There is a lot of manual labor required because examples must be gathered and labeled.
Note: While you can label some anomalies and try to classify them (hence it’s a classification task), the underlying goal of anomaly detection is defining “normal data points” rather than “abnormal data points”. Therefore, it’s hardly ever regarded as a supervised task in real-world applications with few labeled anomaly samples.
Unsupervised
Since neural networks are the most well-known example of unsupervised algorithms, this type of anomaly detection is the most prevalent type.
The amount of manual labor required to pre-process examples can be reduced thanks to artificial neural networks because no manual labeling is required. The use of neural networks is even possible with unstructured data. When dealing with new data, NNs can apply what they have learned to identify anomalies in unlabeled data.
This method has the benefit of reducing the amount of manual work involved in anomaly detection. Additionally, a lot of the time it is impossible to predict every possible anomaly in the dataset. Consider autonomous vehicles as an illustration. On the road, they might run into a situation that has never occurred before. It would be impossible to classify all road conditions into a limited number of categories. Because of this, neural networks are indispensable when using real-time data from the real world.
However, the complexity of ANNs is close to that of rocket science. Therefore, if your project is not very large, you might want to experiment first with more traditional algorithms like DBSCAN.
Furthermore, neural network architecture is a mystery. We frequently don’t know what kinds of events neural networks will classify as anomalies, and they are prone to picking up inaccurate rules that are difficult to correct. Because of this, supervised anomaly detection methods are frequently more reliable than unsupervised ones.
Semi-supervised
The advantages of the first two techniques are combined in semi-supervised anomaly detection methods. When working with unstructured data and automating feature learning, engineers can use unsupervised learning techniques. They do, however, have the chance to watch and manage the kinds of patterns the model learns by combining them with human supervision. The predictions of the model are usually improved by doing this.
Depending on the size of the dataset and the type of issue, various machine-learning algorithms can be used for anomaly detection.
Read More: Semi-supervised Learning
Local Outlier Factor
The method for anomaly detection that is probably used the most frequently is the local outlier factor. The notion of local density serves as the foundation for this algorithm. It contrasts an object’s local density with the densities of the nearby data points. An outlier is a data point that has a lower density than its neighbors.
K-nearest Neighbors
kNN is a supervised A common classification method is machine learning (ML). Since it makes it simple to see the data points on the scatterplot and makes anomaly detection much more understandable, kNN is a useful tool when used to solve anomaly detection problems. The fact that kNN performs well on both small and large datasets is an additional advantage.
In order to solve the classification problem, kNN doesn’t actually learn any “normal” and “abnormal” values. kNN functions as an unsupervised learning algorithm for anomaly detection as a result. An expert in machine learning defines a range of typical and abnormal values manually, and the algorithm automatically divides this representation into classes.
Support Vector Machines
The supervised machine learning algorithm Support Vector Machine (SVM) is another one that is frequently employed for classification. In order to categorize data points, SVMs use hyperplanes in multidimensional space. The threshold for outliers, or nu, is a hyperparameter that must be manually selected.
SVM is typically used when there are multiple classes involved in the issue. However, it is also utilized for single-class issues in anomaly detection. The model can determine whether unfamiliar data falls into this class or is an anomaly because it has been trained to learn the “norm.”
DBSCAN
This algorithm for machine learning without supervision is based on the density principle. By examining the local density of the data points, DBSCAN can find clusters in large spatial datasets and, when used for anomaly detection, generally yields positive results. The points that do not belong to any cluster have their own class: -1, making it simple to spot them. When the data is represented by non-discrete data points, this algorithm manages outliers well.
Autoencoders
This algorithm uses artificial neural networks to compress the data into lower dimensions in order to encode it. The data is then decoded by ANNs in order to recreate the initial input. Because the rules have already been identified in the compressed data, we don’t lose the necessary information when we reduce the dimensionality. We are already able to identify outliers at this time.
Bayesian Networks
ML engineers can find anomalies even in high-dimensional data thanks to Bayesian networks. When the anomalies we’re looking for are more subtle and challenging to spot and visualizing them on the plot might not yield the desired results, we use this method.
Uses Of Anomaly Detection
Let’s now examine some real-world applications for anomaly detection.
Intrusion Detection
For many businesses that handle sensitive data such as client and employee private information and proprietary knowledge, cybersecurity is essential. Network monitoring tools called intrusion detection systems to look for and report potentially malicious traffic. IDS software alerts the team when it discovers suspicious activity. McAfee and Cisco Systems software are two examples.
Fraud Detection
Aiming to obtain money or property illegally is prevented by fraud detection using machine learning. Banks, credit unions, and insurance firms all employ fraud detection software. Banks, for instance, review loan applications before making a choice. The bank employer will be notified if the system determines that some of the documents are fraudulent, such as when it discovers that your tax number doesn’t exist in the system.
Health Monitoring
Systems for anomaly detection are very useful in the medical field. They aid doctors in diagnosis by spotting odd patterns in MRI and test results. Here, neural networks that have been trained on tens of thousands of examples are typically used, and occasionally they provide a diagnosis that is more precise than one made by a physician with 20 years of experience.
Defect Detection
Manufacturing companies risk losing millions of dollars in legal actions by providing clients with defective mechanisms or mechanism components. A plane can crash due to one detail that doesn’t meet production standards, killing hundreds of people.
Computer vision-based anomaly detection systems can identify flaws in detail even when there are thousands of other similar details on the beltline. Additionally, anomaly detection systems can be linked to the controls that keep an eye on internal systems like fuel levels, engine temperature, and other parameters.
Conclusion
Data points that don’t fit the typical patterns are identified through anomaly detection. Numerous issues, such as fraud detection and medical diagnosis, can be resolved with its help. Anomaly detection can be automated and improved with machine learning techniques, especially when large datasets are involved. LOF, autoencoders, and Bayesian networks are some of the typical ML techniques used in anomaly detection.
Read More: Bayesian Machine Learning
