What are the main challenges in anomaly detection?

What are the main challenges in anomaly detection?

Some key challenges include data quality, feature selection, and the balance between false positives and false negatives.

How does data quality affect anomaly detection?

Poor data quality can lead to misleading results, making it difficult to identify true anomalies.

What techniques can be used to improve feature selection?

Techniques like PCA, LASSO, and domain knowledge can help in selecting the most relevant features.

Can you explain the balance between false positives and false negatives?

Yes, it's crucial to minimize false positives to avoid unnecessary alerts, while also reducing false negatives to catch real anomalies.

What are some real-world applications of anomaly detection?

Anomaly detection is used in fraud detection, network security, and monitoring industrial systems.