The term “anomaly detection” refers to methods and software capabilities that automatically identify patterns that deviate significantly from expected or normal behavior. The goal of anomaly detection is to identify irregularities in data streams, IT systems, or business processes at an early stage—for example indications of errors, fraud, security incidents, or operational disruptions—in order to enable a rapid response, reduce risk, and prevent damage.
Data ingestion and preprocessing: Connecting to data sources such as log files, sensors, transaction systems, or APIs, as well as cleaning, normalizing, and aggregating the data.
Modeling normal behavior: Building profiles, baselines, and reference values for normal user, system, or process behavior (e.g., typical access patterns, usual load curves, average transaction amounts).
Rule- and threshold-based detection: Defining static or dynamic thresholds (e.g., number of failed logins, capacity limits) that trigger an anomaly when exceeded.
Statistical anomaly detection: Using statistical methods (e.g., outlier analysis, distribution comparisons, time-series analysis) to identify unusual patterns, trends, or outliers in the data.
Machine-learning-based detection: Applying machine learning algorithms (e.g., clustering, classification models, learning baselines) to detect complex, non-linear anomalies that are difficult to capture with simple rules.
Real-time stream monitoring: Continuous monitoring of log, metric, or sensor streams in real time to detect and flag anomalies immediately.
Scoring, prioritization, and categorization: Assigning anomaly scores, ranking anomalies by severity (e.g., low, medium, high), and mapping them to categories such as “security,” “performance,” “fraud,” or “quality.”
Alerting and notification: Automatically triggering alerts via e-mail, messaging, SMS, or integration into ticketing and incident management systems.
Visualization and dashboards: Providing graphical views of normal behavior, deviations, trends, and historical anomalies in dashboards for quick assessment by domain experts and IT managers.
Root-cause analysis and context enrichment: Allowing drill-down into raw data, correlation of events, and enrichment with context information (e.g., affected systems, users, locations) to better understand the cause of an anomaly.
Feedback and learning mechanisms: Letting users label anomalies as “relevant” or “false positive” so that rules and models can be continuously improved.
Integration with existing systems: Interfaces to monitoring and observability tools, SIEM solutions, ERP, MES, or CRM systems to embed anomaly detection into existing business and IT processes.
A bank monitors credit card transactions and flags unusual spending patterns (e.g., foreign locations or very high amounts within a short period) as potential fraud.
An online retailer detects unusual peaks in failed logins or orders and classifies them as possible bot traffic or attacks on customer accounts.
A manufacturing company monitors machine sensor data (temperature, vibration, power consumption) and identifies unusual values as an indicator of an impending failure (predictive maintenance).
An IT department analyzes logs from servers, applications, and networks to detect suspicious access patterns and data flows as potential security incidents.
An energy provider analyzes smart meter data to discover atypical consumption patterns that may point to meter tampering or technical defects.
An e-commerce company monitors conversion rates, basket values, and return rates to identify sudden deviations in KPIs as an indicator of technical issues or campaign errors.