Everything you need to know about anomaly detection

Anomaly detection is an important aspect in many industries and use cases, from security to networks to optimizing resources or increasing AI capabilities through better data quality.

In this blog post, we look at the various aspects and techniques of anomaly detection and highlight the importance of Machine Learning (ML) and Artificial Intelligence (AI) in this area stands out.

What is anomaly detection?

Anomaly detection is the process of detecting unusual patterns or events within data. These anomalies may indicate errors, fraud, security breaches, or other unexpected events. There are various techniques and methods for anomaly detection based on statistical, machine learning or AI methods.

Anomaly detection and machine learning

The application of machine learning and AI has revolutionized anomaly detection. There are a variety of algorithms and models that have been developed to efficiently and accurately detect anomalies in data. Some examples of machine learning approaches for anomaly detection are Isolation Forest, Autoencoder, and LSTM Autoencoder.

Anomaly detection in time series

Time series anomaly detection refers to the identification of anomalies in temporally ordered data. Here, techniques such as statistical methods, machine learning, and deep learning are particularly useful. For example, LSTM autoencoder anomaly detection can be implemented in time series data using Python to detect unusual patterns.

Unsupervised and supervised anomaly detection

Unsupervised anomaly detection does not use any previous anomaly information, while supervised anomaly detection uses already known anomalies as training data. Both approaches have their own advantages and disadvantages and can be used in different scenarios.

Anomaly detection application examples Anomaly detection can be used in various fields, such as:

• Network Anomaly Detection: Identify security threats and attacks against networks.
• AWS Cost Anomaly Detection: Monitor AWS resource costs and detect unexpected cost increases.
• CloudWatch Anomaly Detection: Monitor the performance of AWS services and detect anomalies in real time.
• Elasticsearch Anomaly Detection: Identify anomalies in large data sets stored in Elasticsearch.
• Prometheus Anomaly Detection: Analyze metrics and detect anomalies in the performance of systems and applications.
• Cybersecurity Anomaly Detection: Detection of security breaches and potential threats in IT systems and networks.

Anomaly detection tools and anomaly detection platforms

There are several tools and platforms that offer anomaly detection capabilities, including:

• Splunk: A powerful platform for analyzing machine-generated data that also provides anomaly detection capabilities.
• AWS Anomaly Detection: A service from Amazon Web Services that uses machine learning to detect anomalies in data.
• Grafana: An open source data visualization and analysis tool that also supports anomaly detection capabilities.
• New Relic: An application performance monitoring platform that provides anomaly detection capabilities.
• Power BI: A business intelligence platform from Microsoft that provides anomaly detection capabilities for data visualization and analysis.

Multivariate anomaly detection

Multivariate anomaly detection refers to the detection of anomalies in multidimensional data. This type of anomaly detection can capture more complex patterns and relationships in the data than univariate anomaly detection approaches. Deep learning techniques such as autoencoders and LSTM autoencoders can be used in multivariate anomaly detection.

Real-time anomaly detection

Real-time anomaly detection refers to identifying anomalies in data as it is generated or collected. This can help identify problems quickly and take action before they escalate. Examples of real-time anomaly detection include network anomaly detection and CloudWatch Anomaly Detection.

Anomaly detection in Python

Python is a widely used programming language that provides many libraries and packages for anomaly detection. Some of the most popular Python libraries for anomaly detection are Scikit-learn, TensorFlow, Keras and PyOD.

Scikit-learn is a popular Python machine learning library that provides several algorithms for anomaly detection. One of these algorithms is Isolation Forest. Here is a simple example that shows how to use Scikit-learn for anomaly detection with the Isolation Forest algorithm:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
from sklearn.datasets import make_blobs
# Generate sample data
X, _ = make_blobs(n_samples=300, centers=1, random_state=42)
# Add some outliers
outliers = np.random.RandomState(42).uniform(low=-6, high=6, size=(20, 2))
X = np.r_[X, outliers]
# Customize the Isolation Forest model
clf = IsolationForest(contamination=0.1, random_state=42)
clf.fit(X)
# Prediction of anomalies
y_pred = clf.predict(X)
# Visualize the results
plt.scatter(X[:, 0], X[:, 1], c=y_pred, s=50, edgecolors='k', cmap='viridis')
plt.title('Anomaly Detection with Isolation Forest')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()

In this example, we first import the required libraries and generate some sample data with make_blobs. We also add some random outliers to the dataset. We then instantiate a IsolationForest-model with a contamination-parameter, which represents the proportion of outliers in the data set. We then fit the model to our data using the predict-method to classify data points as normal or anomalous. Finally, we visualize the results with a scatter plot that uses different colors for normal and anomalous data points.

You can experiment with other anomaly detection algorithms available in Scikit-learn, such as One-Class SVM, Local Outlier Factor (LOF), and Elliptic Envelope. Customize the parameters and dataset to your specific use case.

NLP data and the relevance of anomaly detection in training data.

NLP (Natural Language Processing) refers to the automatic processing and analysis of human language by computers. When working with NLP data, it is critical to use high-quality and consistent training data to develop models that are effective and accurate. Anomaly detection in training data is relevant because it helps identify inconsistent, erroneous, or unexpected data points that could affect model performance.

By detecting and handling anomalies in training data, one can improve the quality of the training data, which in turn leads to better NLP models. This is especially important in applications such as text classification, named entity recognition, sentiment analysis, and machine translation.

Anomaly detection in training data with Konfuzio SDK

To ensure and check for possible outliers among the ground truth annotations, you can use one of the Label class methods. In the following example, we use the Konfuzio SDK and the get_probable_outliers-method to find anomalies in the annotations:

from konfuzio_sdk.data import Project
# Create a project object
project = Project(id_=YOUR_PROJECT_ID)
# Select the desired label
label = project.get_label_by_name(YOUR_LABEL_NAME)
# Find outliers with different anomaly detection methods
outliers = label.get_probable_outliers(project.categories, confidence_search=False)

In this example we use the get_probable_outliers-method to find outliers in the annotations. The method allows to combine different anomaly detection methods or to run them all together and return only the annotations detected by all of them. In this particular case we used the confidence_search-method is explicitly disabled. By default, all three methods are enabled.

With this approach, you can identify and correct inconsistent or erroneous annotations to improve the quality of your training data and ultimately develop better NLP models.

Conclusion on anomaly detection

Anomaly detection is a crucial aspect in many industries and use cases. By using machine learning, AI, and various algorithms, anomalies can be detected efficiently and accurately. The continuous evolution of these techniques and methods allows us to develop better and better anomaly detection systems to address challenges and threats in real time.