Rule-Based Detection
Rule-based detection is a methodology in software development and data analysis where predefined rules or conditions are used to identify patterns, anomalies, or specific events in data. It involves creating explicit logical statements (e.g., if-then rules) that trigger actions or alerts when certain criteria are met, commonly applied in areas like fraud detection, security monitoring, and data validation. This approach is deterministic, relying on human-defined rules rather than machine learning models.
Developers should learn rule-based detection for scenarios requiring high interpretability, low latency, and regulatory compliance, such as real-time fraud prevention in financial systems or security event monitoring in IT operations. It's particularly useful when domain knowledge is well-established and the detection logic needs to be transparent and easily auditable, as in compliance checks or simple automation tasks.