Anomaly detection identifies deviations from the norm. Think of it like detecting a fever in the human body: We know something is wrong even before the temperature exceeds a certain threshold. Similarly, in digital systems, anomalies — deviations from typical behavior — can be early indicators of problems like fraud, protocol aberration, equipment failure or cyberattacks.
What Is Anomaly Detection?
Anomaly detection involves finding unexpected deviations from a typical pattern. Whether it’s identifying an odd entry in a table of data or flagging unusual behavior in network traffic, anomaly detection uncovers the unexpected items that often hold the key to safeguarding systems.
Why Is Anomaly Detection Important?
Anomalies act as red flags, signaling critical events or risks that require attention. Untoward incidents don’t happen all of a sudden. They’re waiting to happen! Let’s think about this using real-life scenarios.
If my credit card were fraudulently used, I would be stressed and worried. Now, imagine if the credit card team proactively detected and prevented such fraud by identifying anomalies in transactions. The relief I’d feel is immeasurable.
Similarly, in healthcare, anomaly detection plays an equally vital role. As the saying goes, “An ounce of prevention is worth a pound of cure.” By identifying anomalies and pairing them with expert medical insights, healthcare professionals can achieve early diagnoses and suggest lifestyle changes to prevent the onset of chronic diseases. This is a powerful tool that can save lives and improve quality of care.
These examples highlight the importance of early intervention in safeguarding trust and prevention.
What Does Anomaly Detection Do?
Anomaly detection tools identify unusual patterns or behaviors that deviate from the norm. These tools typically rely on a combination of approaches to pinpoint anomalies effectively.
Threshold Monitoring
Setting baseline parameters and flagging deviations.
Pattern Recognition
Identifying unusual sequences of events or behaviors.
Relationship Analysis
Understanding connections between various system components.
Multi-Dimensional Analysis
Examining data from multiple perspectives to validate anomalies.
Domain Expertise
Collaborating with industry experts to ensure accurate interpretation of findings.
Together, these methods create a robust framework to detect and address anomalies efficiently across various industries.
Benefits of Anomaly Detection
The real value of anomaly detection lies in its ability to prevent problems before they escalate.
Sharing one of my previous experiences as an example, anomaly detection and data analysis of fuel consumption led to the discovery of potential fuel theft, which led to fewer losses and higher operational efficiency.
In another case with automotive warranty claims, some car dealers would find loopholes and break the rules, which could become unfair to both parties. Rule-based systems simply couldn’t keep up. This is precisely why anomaly detection has become indispensable. Benefits and characteristics of such systems include the following.
Dynamic Learning
Adapting to changing patterns and behaviors over time. For instance, if dealers start submitting claims for new types of repairs, the system learns and adjusts its detection criteria accordingly.
Subtle Correlation Detection
Identifying relationships across multiple dimensions may not be immediately obvious. For example, anomaly detection can spot patterns such as a specific dealer consistently submitting warranty claims for parts that rarely fail under normal conditions or identifying correlations between vehicle mileage and the likelihood of certain claims being fraudulent.
Real-Time Analysis
Processing massive streams of data in batch processing, such as vehicle tracking information, can detect false warranty claims. For instance, it can flag a claim for a repair if the vehicle in question was never brought to the service center or if the repair date doesn’t align with the vehicle’s location history.
Cost Savings
Preventing issues before they escalate, saving resources and reducing downtime. By detecting aberrations in warranty claims early and sharing the insights with the audit team, the system sent a clear message to the dealer engagement and reduced the administrative burden of investigating disputes later.
Challenges of Anomaly Detection
Detecting anomalies comes with its own set of challenges.
Possibilities of False Positives
In scenarios like warranty claims aberrations, labeling something as fraud can strain the relationship between OEMs (Original Equipment Manufacturers) and vendors. Additionally, false positives — where legitimate behavior is flagged as anomalous — can lead to awkward and embarrassing situations.
Data Quality Challenges
Sometimes, the problem lies in the data quality itself. For instance, a faulty sensor might generate incorrect readings, or the data may be incomplete or noisy, making it harder to identify true anomalies. These issues can bury anomalies within irrelevant or misleading data, complicating detection efforts.
Continuous Feedback Needed
For anomaly detection models to improve over time, they need continuous feedback and monitoring. This step is often overlooked once the model is deployed in production, however. On the other hand, much like people, models become more efficient over time if the right error metrics are measured and used to refine them.
Diverse Data Dimensions
Anomaly detection systems require data from various sources to identify rare events effectively. Unfortunately, not all necessary data is always available when needed. Typically, ETL (Extract, Transform, Load) or engineering teams gather data for reporting purposes, but anomaly detection data scientists must ensure that the right features are included in the data lake to support their models.
Spotting rare events also needs sophisticated methods. It often involves iterating through different data transformations and algorithms to isolate these rare occurrences. This process takes time and demands close collaboration between statisticians, data scientists and business analysts. Without this teamwork, it’s difficult to develop robust solutions for detecting anomalies.
Anomaly Detection Methods
Anomaly detection involves identifying data points, events or observations that deviate significantly from the norm. Depending on the availability of labels in the data set, anomaly detection can be categorized into supervised, unsupervised, or semi-supervised approaches. Let’s explore some standard methods used in anomaly detection:
Unsupervised Anomaly Detection
When labels aren’t available, unsupervised methods are commonly used, which rely on patterns and statistical properties of the data to identify anomalies.
Statistical Methods (Mean and Standard Deviation)
A simple and widely used approach involves calculating the mean and standard deviation (SD) of the data. Using the properties of a normal distribution, any value that lies beyond three standard deviations from the mean is flagged as an anomaly. One can use this method in cases of transaction data to detect unusually high or low values. Also note that, although I have used “anomaly” and “outlier” interchangeably in this context, an outlier is specifically when a certain transaction is abnormally large.
Isolation Forest
Think of anomaly detection like catching fish with a net. The net lets the water (normal data) pass through while catching the fish (anomalies). An isolation forest works similarly by recursively splitting the data points and isolating outliers. Points that are well-connected (normal data) require deeper splits, while anomalies are isolated quickly. This method is more efficient and effective for high-dimensional data.
Clustering Methods (K-Means, DBSCAN)
Clustering algorithms like K-means or DBSCAN can also be used for anomaly detection. These methods group data points into clusters based on similarity. Points that don’t belong to any cluster or are far from cluster centroids are flagged as anomalies. These methods require careful parameter tuning to achieve the desired results, however.
Transformations to Simplify Detection
Transforming data into a new space can make anomalies easier to detect. For instance, applying Fourier transformations can highlight unusual patterns in time-series data by analyzing frequency components. Similarly, dimensionality reduction techniques like principal component analysis (PCA) or t-SNE can project data into lower-dimensional spaces, where anomalies often become more apparent.
Supervised Anomaly Detection
When labels are available, supervised learning methods can be employed to classify data points as normal or anomalous.
Classification Algorithms
Standard classification algorithms like logistic regression, decision trees, or support vector machines (SVMs) can be used to detect anomalies when labeled data is available. These models learn from historical data to predict whether a new data point is anomalous or not.
Ensemble Methods
Combining multiple algorithms, such as random forests or gradient boosting, can improve anomaly detection accuracy. Ensemble methods draw on the strengths of diverse approaches to create a more robust detection system.
Synthetic Data for Rare Anomalies
Since anomalies are rare by nature, training models on imbalanced data sets can be challenging. One effective approach is to introduce synthetic data to balance the data set. Synthetic anomalies can be generated to train the model, helping it learn to detect rare events more effectively.
Iterative Approach and Collaboration
Spotting rare events often requires sophisticated methods and iterative experimentation. Data scientists and statisticians may need to try different data transformations and algorithms to isolate anomalies effectively. This process takes time and requires close collaboration with business analysts to ensure the results align with real-world expectations.
Anomaly Detection Techniques
Here’s a breakdown of popular techniques.
Visualization
Visualization uses tools like scatter plots and heatmaps to identify outliers visually.
Example Use Case for Visualization
Clustering outliers in sales transactions.
Statistical Tests
Statistical methods like z-scores detect anomalies based on statistical thresholds.
Example Use Case for Statistical Tests
Identifying extreme weather temperature readings.
Distance-Based Algorithms
Distance-based algorithms flag outliers based on their distance from neighboring points.
Example Use Case for Distance-Based Algorithms
Detecting unusual customer locations for online purchases.
Density-Based Algorithms
Density-based algorithms analyze low-density regions to spot outliers.
Example Use Case for Density-Based Algorithms
Identifying rare cyberattack patterns in network logs.
Frequent Item Set Algorithms
Frequent item set algorithms highlight deviations from frequent patterns in data.
Example Use Case for Frequent Item Set Algorithms
Detecting irregular purchase patterns in retail.
Dimensionality Reduction
Dimensionality reduction simplifies high-dimensional data to isolate anomalies.
Example Use Case for Dimensionality Reduction
Conducting PCA to identify faulty equipment sensors.
Synthetic Data Generation
Synthetic data generation creates artificial data to train models for rare anomaly scenarios.
Example Use Case for Synthetic Data Generation
Training fraud detection systems with simulated data.
For some of the Python-based implementations, check out PyOD library which has more than 50 detection algorithms.
Types of Anomalies
Anomaly detection involves identifying three main types of anomalies.
Point Anomalies
These are individual data points that significantly deviate from the norm. For instance, a speed of 200 mph in city traffic would be a clear point anomaly.
Contextual Anomalies
These are anomalies that are unusual only within a specific context. For example, a temperature of 95°F might seem normal, but in the context of a winter day in Alaska, it becomes anomalous.
Collective Anomalies
These occur when a group of related data points collectively deviates from expected patterns. For instance, multiple failed login attempts followed by access from a foreign location could signal unauthorized access.
Anomaly Detection Use Cases
Anomaly detection has versatile applications across industries.
IT and DevOps
Use cases include intrusion detection (system security, malware), production system monitoring or monitoring for network traffic surges/drops. Challenges include the need for a real-time pipeline to react and huge volumes of data, plus the unavailability of labeled data corresponding to intrusions, making it difficult to train/test. Here, you usually have to adopt a semi-supervised or unsupervised approach.
Manufacturing/Industry/Construction/Agriculture
Use cases here include predictive maintenance and service fraud detection. Challenges include the fact that industrial systems often produce data from different sensors that vary immensely, such as different levels of noise, quality and frequency of measurement.
Healthcare
Healthcare applications include condition monitoring, including seizure or tumor detection. Difficulties include the fact that the costs of misclassifying anomalies are very high. Also, labeled data more often than not belongs to healthy patients, so you usually have to adopt a semi-supervised or unsupervised approach.
Finance and Insurance
Applications in finance and insurance include fraud detection (credit cards, insurance, etc.), stock market analysis and early detection of insider trading. Financial anomaly detection is high-risk, which requires real-time detection to stop it as soon as it happens. Unlike other cases, false positives can happen here, which may disrupt user experience.
Public Sector
Public sector applications include the detection of unusual images collected from surveillance. Because this type of anomaly detection requires deep learning techniques, it is more expensive.
Frequently Asked Questions
Who uses anomaly detection?
Professionals across industries like finance, healthcare, manufacturing and cybersecurity use anomaly detection. For exam ple, in fintech, companies often ask if they can identify outliers before approving loans or detect anomalies in loan collections. This is an extra layer of protection on top of the risk assessment systems they already have. Similarly, insurance companies use anomaly detection to flag suspicious claims.
What is anomaly detection used for?
Anomaly detection is used to identify unusual patterns or behaviors in systems, with applications ranging from detecting fraud and preventing security breaches to improving operational efficiency and ensuring data quality. It helps flag potential issues early, acting as a warning system before major problems occur.
