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:
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ML classifiers – Any classifier with class imbalance support
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ML anomaly detection methods – iForest, one-class SVM, KNN
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Statistical methods – KDE, generalized ESD
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Auto-encoders – MLP
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Sequence predictors – LSTM
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Clustering – K-Means, DBScan
