Machine learning is being used in a variety of domains to restrict or prevent undesirable behaviors by hackers, fraudsters and even ordinary users. Algorithms deployed for fraud prevention, network security, anti-money laundering belong to the broad area of adversarial machine learning where instead of ML trying to learn the patterns of benevolent nature, it is confronted with a malicious adversary that is looking for opportunities to exploit loopholes and weaknesses for personal gain.
Some current approaches to adversarial tasks include:
ML classifiers - Any classifier with class imbalance support
ML anomaly detection methods - iForest, one-class SVM, KNN
Statistical methods - KDE, generalized ESD
Auto-encoders - MLP
Sequence predictors - LSTM
Clustering - K-Means, DBScan