Anomaly detection is used to identify patterns, behaviors, or data points that deviate significantly from normal or expected behavior. It is commonly employed in fields like cybersecurity, fraud detection, manufacturing, and healthcare.
In cybersecurity, anomaly detection systems monitor network traffic to flag unusual activities that might indicate security breaches, such as unauthorized access or malware. Similarly, in fraud detection, banks use anomaly detection to identify suspicious transactions, such as large withdrawals or purchases in unfamiliar locations.
In manufacturing, anomaly detection helps identify defective products or machinery issues by analyzing production data. This proactive approach prevents downtime and reduces costs. In healthcare, it is used to detect irregularities in medical scans or patient vitals, aiding in early diagnosis of diseases.
Anomaly detection techniques range from statistical methods, like Z-scores or moving averages, to machine learning models, such as autoencoders and isolation forests. Advanced systems also leverage time-series analysis to monitor trends over time.
The effectiveness of anomaly detection depends on the quality of data and the chosen model. Accurate detection minimizes false positives and ensures timely interventions, making it invaluable across industries.