An irregular or unexpected pattern within a data set can be an early warning sign for organizations, signaling an issue that needs to be resolved quickly. From identifying disease markers to uncovering network intrusions, anomaly detection is an essential practice in many industries. Advances in machine learning (ML) have ushered in a new generation of anomaly detection that is significantly more capable than its rules-based predecessor. In this article, we’ll explore what ML-based anomaly detection can do and share how Snowflake democratizes access to ML-enabled insights, including anomaly detection functions.
Rules-Based Versus ML-Based Anomaly Detection
There are two main approaches to developing anomaly detection systems: ML-based and rules-based.
Rules-based techniques rely on a set of predefined rules and patterns, triggering an alert when a certain condition is met. As the name implies, rules-based anomaly detection is inflexible and can require significant resources to update and maintain.
ML-based anomaly detection relies on powerful algorithms that automatically learn normal patterns in large, high-dimensional data sets and use this information to detect deviations. They have the ability to work autonomously, continuously learning and adapting as conditions change. The longer they’re in use, the more accurate they become in comparing expected baseline activity against unusual patterns or clusters among variables. Machine learning anomaly detection algorithms include both supervised and unsupervised learning methods.
Popular supervised models used in anomaly detection include random forest, decision tree and K-Nearest Neighbors. Isolation Forest and Local Outlier Factor are two examples of unsupervised ML models that can be used to detect deviations.
Machine Learning Anomaly Detection Use Cases
Significant gains in machine learning have expanded the potential use cases for this technology, providing new opportunities across industries. Here are just a few examples.
Fraud detection
ML-based anomaly detection helps financial institutions identify fraudulent transactions, money laundering and insurance fraud. Advanced ML models profile normal user behavior and detect outliers, often in seconds or less. Their ability to learn over time makes these tools highly adaptable, allowing them to detect specific types of fraud.
Cybersecurity
Anomaly detection plays an important role in strengthening an organization's cybersecurity stance. Compared to signature-based detection, which identifies known threats, anomaly-based detection tools are highly effective at detecting unknown threats. Examples include potential signs of network intrusion, such as unauthorized devices being added to a network, a large number of new IP addresses trying to establish a network connection or an employee attempting to access restricted resources at an atypical time. When one of these indicators is detected, the system automatically triggers an alert, allowing security teams to investigate and intervene. ML-based anomaly detection is especially useful in a security context because it’s able to learn and adapt, evolving as cybercriminals switch tactics.
Medical imaging
The healthcare industry uses anomaly detection extensively. One example is remote patient monitoring. This technology can be used to detect unusual patterns in patient health data — such as health rate or blood glucose levels — that may indicate the need for timely medical intervention. ML-based anomaly detection is also being used to provide decision support to physicians, helping them identify difficult-to-detect medical conditions, such as certain types of cancers, with greater accuracy.
Manufacturing
IoT sensors from industrial machines and manufacturing lines continuously stream data, including the performance metrics of individual machines and product-quality data. ML-based anomaly detection tools analyze this data, helping operators proactively identify impending equipment failures, and allowing time for the required parts and mechanical expertise to be requisitioned. Anomaly detection also plays an important role in quality control, quickly spotting manufactured products that deviate from established quality standards. The capabilities of machine learning are valuable in this context because ML models reduce false positives by learning from the data and adapting to variations in normal behavior.
Smart cities
Remote air quality sensors allow cities and other municipalities to identify areas with poor air quality, and then provide this information to citizens to raise awareness and promote more sustainable behaviors. Hyper-local air quality monitoring helps citizens make more-nformed decisions, such as when to exercise, or whether to walk or drive. ML-based anomaly detection can handle massive volumes of data, so additional data — such as satellite imagery, weather conditions and pollutant levels — can be included for more accurate results.
Unlock ML aAnomaly Detection with Snowflake Cortex
Traditionally, AI and ML innovation was limited to a select few experts within an organization who could develop insights. Snowflake Cortex changes this — as an intelligent, fully managed service that helps organizations analyze data and quickly build AI applications, all within Snowflake. Moreover, it democratizes AI and ML for all, including SQL users and data analysts.
With Snowflake Cortex ML Functions, and specifically the Anomaly Detection ML function, organizations can easily identify outliers amid their time-series data. Users need not learn Python, have expertise in ML algorithms or manage infrastructure — making machine learning anomaly detection and other ML functions readily accessible to a non-technical audience. Further, they can leverage ML functions wherever they access data, either in Snowsight or using their favorite SQL editor.
Machine learning anomaly detection allows analysts to capitalize on the rapidly expanding volumes of available data, helping them generate more accurate insights, faster. With Snowflake Cortex, leaders across industries can make smarter, better-informed decisions.