What are Anomalies?
Anomalies are instances in data that significantly differ from most of the dataset. They are also named ‘outliners’. The anomalies may be rare events, looking for something different, or errors in the data. They can also uncover major flaws like fraud, system dysfunctions, or operational issues.
Hence, detecting anomalies in the data is very important. If we find an anomaly, it must be removed before creating a model because it can alter the statistics or hurt the accuracy of machine learning training.
What is Meant by Anomaly Detection in Machine Learning?
Anomaly Detection, as the name suggests, is detecting an anomaly in some set of data points or observations. This process helps you find abnormal events that may require further investigation. Machine learning algorithms can automate anomaly detection, making it more scalable and effective than manual techniques. Through data analysis, machine learning anomaly detection techniques learn typical patterns and highlight those that are not.
Types of Anomalies
Selecting the appropriate detection technique requires understanding the various kinds of anomalies. Point anomalies, contextual anomalies, and collective anomalies are the three main categories into which anomalies fall.
1. Point Anomalies
Point anomalies are individual data points deviate significantly from the rest of the data. Easiest to detect of all anomaly types. In the human heights example, consider a point whose height is 8 feet—this entry will be known as a ‘point anomaly,’ which by its nature of being too tall cannot most likely happen in practice.
2. Contextual Anomalies
A data point is considered contextually anomalous if it is abnormal in one context but not in another. These anomalies are more complex because the context must be considered. For instance, a temperature reading of 30°C might be normal in summer but anomalous in winter. Contextual anomalies are common in time-series data where the context (e.g., time of day, season) is important.
3. Collective Anomalies
Collective anomalies are a set of data instances whose aggregate dissension from the rest in the collection alone is unusual. Though each constituent point in the group may not be an anomaly, their overall combined activity stands out. This collective anomaly may indicate a cyberattack if network traffic suddenly increases. Detecting collective anomalies may require looking at all data points to identify such examples.
Anomaly Detection Techniques
Anomalies can be found using a variety of methods, which are generally divided into supervised and unsupervised categories.
1. Unsupervised Methods
This works well with an unsupervised approach where not much prior knowledge is available about the anomalies and they don’t have labeled data for the model to understand its characteristics. These methods take advantage of the inherent structure in data to identify patterns or anomalies.
- Clustering-Based Methods: These methods group data points that are similar in nature. Anomalies are the data points that do not lie in any cluster or lie only in small clusters. Some examples include k-means clustering and DBSCAN.
- Density-Based Methods: These methods primarily treat data points as stations in space. Any points that reside outside are anomalies. Local Outlier Factor (LOF) is a widely used density-based approach.
- Statistical Methods: These methods take the statistical distribution of data into account. The distribution of points in this space can be seen as the core generative process that defines normal behavior, while those who fall away from said structural shape (let’s say a Gaussian bubble) are identified and labeled by anomaly detectors, such as Gaussian models or z-score.
- Isolation Forest: This algorithm isolates observations by randomly selecting a feature and then splitting it randomly between its maximum and minimum values. The fewer splits required to isolate a point, the more likely it is an anomaly.
2. Supervised Methods
If you know the anomaly labels, supervised methods are used. These algorithms learn from the given labeled data and, with that help, predict the abnormality of new incoming streaming fields. The four broad categories of existing anomaly detection techniques are:
- Classification-Based Methods: These methods train a model to identify normal and abnormal data. For example, decision trees, support vector machines, and neural networks.
- Regression-Based Methods: These methods predict a value, which helps with finding deviations of that prediction from the actual result, serving as anomalies. Examples include linear regression or polynomial regression.
- Neural Networks: For more advanced techniques, you can use an autoencoder to recognize anomalies. Autoencoder learns how to encode data and then decode it back. Anomalies are those points where the reconstruction error is high.
- Ensemble Methods: When using ensembles, we can potentially increase the robustness of anomaly detection by increased variance. Examples include Gradient Boosting Machines and Random Forests, which are widely used.
5 Best Anomaly Detection Algorithms
One of the most important steps in accurate anomaly detection is selecting the right algorithm. Here are five of the best anomaly detection algorithms:
1. Isolation Forest:
Forest isolates observations by choosing a feature at random and dividing it into its maximum and minimum values. The fewer splits needed to isolate a point, the more likely it is to be an anomaly. This effective algorithm is a popular choice for many applications because it performs well with high-dimensional data.
2. Local Outlier Factor (LOF):
LOF measures the local density deviation of a given data point concerning its neighbors. Anomalies are data points that deviate significantly from others and are seen as outliers. LOF is excellent for high-density datasets with varying distributions of data points.
3. One-Class SVM:
he One-Class Support Vector Machine (SVM) learns to separate normal data points from anomalies. It works well for high-dimensional data, especially when the anomaly class is much smaller compared to the regular class. One-Class SVM is robust for scenarios where anomalies are rare.
4. Autoencoders
Autoencoders, a type of neural network used for unsupervised learning, compress data into a lower dimension and then decode it back. Anomalies are points with high reconstruction errors. Autoencoders are great for detecting anomalies in high-dimensional data like images and complex time series.
5. k-Nearest Neighbors (k-NN)
k-NN calculates the distance of a point to its nearest neighbors. Large distances indicate outliers. This method is intuitive for identifying anomalies in a non-parametric and easy learning setup, especially when the data distribution is well understood and has low dimensionality.
Use Cases for Anomaly Detection
Applications of anomaly detection span several industries. A few examples include:
- Fraud Detection: In the banking and finance sector, anomaly detection can alert when someone spends more than usual, indicating fraud.
- Network Security: With the help of identifying anomalous traffic patterns in a specific network segment, it is easier to pinpoint suspicious cyber threats. These algorithms catch such anomalous events, for example, a spike in data transfer or unusual login attempts.
- Healthcare: Monitoring patient vitals and detecting early signs of health issues is another crucial application. Early anomaly detection, looking for unusual heart rates, blood pressure, or other vital signs, allows for early intervention.
- Manufacturing: Product defects and faulty parts need to be detected accurately in the factory for quality control purposes, as well as cost-effectiveness. The sensors record temperature, pressure, and vibration of machines. These algorithms for anomaly detection search for small changes, indicating equipment is potentially going to fail.
- Finance: Identifying odd price changes or market anomalies can aid investors and analysts. Anomalies in financial data suggest special market events or abnormal trading behavior.
- Retail: Finding unusual sales trends, such as a sudden increase or decrease in demand for certain products, can help with stock control and demand prediction.
Anomaly Detection Examples
In the following section, we break down some real-world examples to better understand anomaly detection and its capabilities:
Credit Card Fraud Detection:
Unsupervised machine learning anomaly detection models can catch unusual behaviors, such as a person spending significantly less than usual. An example is an unforeseen high-value transaction in another country, which can be flagged as fraud. These models are used by banks and financial institutions to safeguard their customers from loss.
Industrial Equipment Monitoring:
Machinery-based sensors gather valuable data on temperature, pressure, and vibration parameters. Anomaly detection algorithms can identify issues like equipment breakdowns, where values fall well outside the expected range. This allows for predictive maintenance, reducing downtime and costs.
Network Intrusion Detection:
In cybersecurity, anomaly detection scrutinizes network packets for irregular occurrences. A sudden spike in data transfer or repetitive failed logins could indicate unwanted activity, potentially leading to an attack. These models help prevent unauthorized access and protect information.
Healthcare Monitoring:
Hospitals use anomaly detection to monitor patient vitals in real time. Sudden changes in heart rate, blood pressure, or oxygen levels could be critical warning signs. Prompt detection allows for early intervention, improving patient outcomes.
Retail Sales Analysis:
Retailers use anomaly detection to study sales data and uncover unusual trends. A sharp spike in sales of a specific product could indicate a successful marketing strategy, while a sudden decrease might point to supply chain issues or shifts in customer demand.
Challenges in Anomaly Detection
The reality is that despite its significance, an anomaly detection system comes with several challenges:
- Data Quality — Low-quality data could lead to faulty detection methods. However, the performance of the anomaly detection models can be hampered by missing values, noise, and inconsistencies.
- Balanced Data — Abnormal values are rare with respect to normal data, causing an imbalance in the dataset. This imbalance is what makes it difficult for models to learn and detect anomalies correctly.
- High Dimensionality — Anomaly detection becomes more complicated with high-dimensional data. The more features there are, the sparser the data becomes, making it difficult to detect patterns and anomalies.
- Time: Dynamic Data — The data in many applications is constantly generated and changes over time. When we apply anomaly detection models to dynamic data streams, the application of real-time processing causing changes to anomalies is crucial.
- Understanding of Context — This type requires context to be known in advance, and then it can be used as a contextual normal or anomaly. This makes it difficult to detect as sometimes the context is not easily understandable.
Future Directions in Anomaly Detection
The field of anomaly detection is evolving, with several promising directions for future research and development:
- Deep Learning: More advanced deep learning models (CNNs, RNNs) are being explored. Such models can learn intricate pattern information and relationships in the data.
- Interpretability: It is important to know why a data point is defined as an outlier in order to trust and admit the model. The research is concentrated on generating transparent and interpretable models for anomaly detection.
- Transfer Learning: Transfer learning corresponds to using one (or more) domain(s) as the training base for anomaly detection in another. This can be useful in situations where labeled data or anomalous patterns are sparse.
- Adversarial Learning: Adversarial learning can greatly improve the stability of a model in anomaly detection. Adversarial examples are used for training models to better recognize the difference between normal and anomalous data.
- Integration with Domain Knowledge: Combining machine learning models trained on top of domain-specific knowledge can enhance anomaly detection. Experts might provide useful information and context to improve its actuality and accuracy.
Conclusion
Organizations can take advantage of machine learning anomaly detection for better security, faster workflows, and data-backed decisions. The objective is to detect and deal effectively with anomalies, whether we are doing it unsupervised or supervised.
The scope of impact and applications using anomaly detection models and algorithms is enormous, with several state-of-the-art technology solutions. Anomaly detection is essential in many areas, including but not limited to fraud detection, network security, the healthcare industry, or the manufacturing sector, as it can help ensure system integrity and production efficiency.
