Anomaly detection
- See also: Machine learning terms
Introduction
Machine learning Anomaly detection is the process of recognizing data points that deviate from normal behavior in a dataset. These abnormal outcomes are known as anomalies, outliers, or exceptions. Anomaly detection plays an integral role in many domains such as fraud detection, network intrusion detection, and fault detection in industrial systems.
Applications
Anomaly detection is used in many fields to detect and prevent potentially hazardous events. Some of its applications include:
- Fraud detection: In finance, anomaly detection is employed to spot fraudulent transactions. For instance, a credit card company could utilize anomaly detection to spot purchases that deviate from typical spending patterns of an individual customer.
- Network Intrusion Detection: Anomaly detection can be employed to detect network intrusions by monitoring network traffic for any deviations from normal behavior that might indicate an attack. This method monitors network activity to detect anomalies that might indicate an attack has taken place.
- Fault detection in industrial systems: Industrial systems utilize anomaly detection to identify faults in equipment. For instance, a manufacturing plant can utilize anomaly detection to recognize when a machine deviates from its usual operating parameters.
Techniques
There are various techniques used for anomaly detection, such as statistical methods, machine learning algorithms and data mining methods. Some of the most popular ones include:
Statistical Methods: Statistical methods assume the data follows a particular distribution, such as a Gaussian distribution. They calculate the mean and standard deviation of the data points to identify those that deviate significantly from it.
- Machine Learning Algorithms: Machine learning algorithms such as clustering, classification and deep learning can be employed for anomaly detection. Clustering algorithms identify clusters of similar data points; any that do not belong to one are considered anomalies. Classification algorithms label data as normal or abnormal and train their algorithm to recognize patterns associated with each label. Deep learning techniques like autoencoders also work to detect anomalies by reconstructing input data and comparing its reconstruction error with a threshold value.
- Data Mining Techniques: Data mining techniques such as association rule mining and deviation detection can be employed for anomaly detection. Association rule mining helps identify relationships between variables in the data, while deviation detection identifies data points that deviate significantly from expected values.
Explain Like I'm 5 (ELI5)
Anomaly detection is like playing detective, trying to uncover things that are out of the ordinary or out of place. Imagine you have a bag full of candy with mostly red and yellow candies but one green piece - an anomaly! That green candy stands out as different from all the rest and should be considered an anomaly. In machine learning terms, anomaly detection helps us recognize anomalous data points like that green candy in among all those other treats!
Explain Like I'm 5 (ELI5)
Absolutely! Anomaly detection is the process of recognizing something different among a collection of objects.
Imagine you have a large basket of apples, all red except for one green apple. This green apple stands out from the others and stands out as an "anomaly"--it is different from all others.
Machine learning works similarly, taking a group of data points (like an apple basket) and analyzing their values according to features like weight, size, and shape. Finally, we identify any anomalies - like the green apple in the basket - which stand out from others.
Anomaly detection is used in many fields, such as finance, healthcare and security to detect things that are anomalous and require attention.
- See also: Machine learning terms
Introduction
Anomaly detection is a subfield of machine learning that seeks to identify rare events, outliers or abnormalities in datasets that deviate significantly from the majority. These anomalous instances may represent interesting or critical data points such as fraudulent transactions, medical diagnosis, manufacturing defects, system failures and network intrusions, among others. Anomaly detection presents challenges due to its often rarity, irregularity and complex definition depending on the application domain.
Types of Anomalies
Anomalies can be divided into three primary categories: point anomalies, contextual anomalies and collective anomalies.
=Point Anomalies
Point anomalies, also referred to as global anomalies, refer to individual data points that differ significantly from the majority. Examples of point anomalies include credit card fraud, sensor glitches and network intrusions. They can be detected using statistical methods like the z-score, interquartile range or Mahalanobis distance; or machine learning techniques like isolation forest, one-class SVM or autoencoder.
=Contextual Anomalies
Contextual anomalies, also referred to as conditional anomalies, refer to data points that are anomalous only within certain contexts or subpopulations of the data. For instance, a high heart rate may be considered normal during physical exercise but abnormal when sleeping. To detect contextual anomalies, context information must be integrated into the equation; this can be done through rule-based systems, Bayesian networks or decision trees.
=Collective Anomalies
Collective anomalies, also referred to as group anomalies, refer to a collection of data points that exhibit unusual behavior when taken together but not individually. Examples include sudden spikes in web traffic or power outages in an area. To detect collective anomalies requires the detection of patterns or dependencies between data points and identification of subpopulations that show anomalous behaviour. Clustering, principal component analysis or local outlier factor can all be utilized for detection.
Challenges of Anomaly Detection
Anomaly detection presents several obstacles, making it a complex and often unsolved challenge.
=Data Imbalance
One of the major obstacles lies in data imbalance, where anomalies make up a small fraction of all instances compared to normal data points. This makes it difficult for machine learning models to learn characteristics about anomalies and distinguish them from regular instances.
=Labeling
Another challenge lies in labeling, where labeled anomalies may be scarce or unavailable and the definition of what constitutes an anomaly may be uncertain or context dependent. To address this, unsupervised or semi-supervised techniques that do not require labeled data may be utilized instead, along with expert knowledge and feedback to refine the definition of anomalies.
=High Dimensionality
Anomaly detection often faces the problem of high dimensionality, where data may contain many features or variables that make it challenging to detect anomalies and visualize them. To address this challenge, feature selection, dimensionality reduction techniques or visualization strategies can be employed in order to simplify the data and focus on the most pertinent ones.
=Concept Drift
Another difficulty is concept drift, in which the distribution of data alters over time and makes a model outdated or ineffective at detecting new anomalies. To combat this problem, adaptive or online learning techniques such as reinforcement learning should be utilized that update models in real-time or adapt to changes in data distribution.
Applications of Anomaly Detection
Anomaly detection has numerous applications in finance, healthcare, manufacturing, security and environmental monitoring.
Finance utilizes anomaly detection to identify fraudulent transactions, credit card fraud, money laundering activities and insider trading activities.
What an exciting opportunity!
Explain Like I'm 5 (ELI5)
Anomaly detection is like playing "spot the difference", only with computers instead of pictures.
Imagine you have a collection of pictures of cute animals, like cats and dogs, all looking similar. But in one picture there's an oddball who stands out from the others - an anomaly!
Similar to machine learning, we teach computers how to spot patterns in data. We give them numerous examples of what we consider "normal" or "typical" data and then give them new data to examine, the computer attempts to discern patterns that match what it learned from the previous examples.
If the new data looks very much like what was learned as "normal", the computer says "everything looks fine, no anomalies here!" However, if it diverges significantly from what had previously been considered "normal", the machine may say "this looks strange; maybe there's an anomaly here!"
Anomaly detection can help us uncover problems or errors in data that we might otherwise overlook. It's like having an invisible superpower that enables us to identify things that are out of the ordinary!