Anomaly detection is the process of identifying unexpected behaviours within a dataset by leveraging Machine Learning and statistical analysis.
Typically, they help in detecting deviations from normal system or application behaviour. For instance, an anomaly detection model running on APM metrics of an application can help identifying a new type of error log that has appeared in the system.
Anomaly detection is vital for proactive monitoring, troubleshooting, and maintaining system reliability and performance. It is not dependent on pre-defined threat knowledge.
1. False Positives and False Negatives: Detecting the normal behavior as an anomaly, or vice-versa.
2. Data Quality and Imbalance: Anomaly detection needs high-quality reliable data and data sets where the anomalies are rare, and can challenge the model.
3. Threshold Selection: Setting appropriate thresholds for anomaly detection can be tricky and may require fine-tuning.
4. Interpreting Results: Understanding why an anomaly was detected and what caused it can be complex, requiring deep domain knowledge.
5. Scalability: Applying anomaly detection to large-scale, real-time data can strain computational resources.
Read more about Threat Detection, Triaging, and On-call Rotation
