Why do you need machine learning for anomaly detection?

Compared to traditional methods, machine learning solutions for anomaly detection show a lower rate of false positives, enhance performance as they process new data, and better deal with new types of anomalies.

What are the approaches to ML-based anomaly detection?

Anomaly detection with machine learning can take three approaches, depending on the training technique used to teach an algorithm to identify anomalies: supervised, unsupervised, or semi-supervised.

Which machine learning algorithm is used to detect anomalies?

Machine learning engineers can count on various machine learning and deep learning algorithms, including a support vector machine, decision tree, random forest, logistic regression, k-nearest neighbor, and different types of neural networks.

Machine Learning for Anomaly Detection: Use Cases and Guidelines