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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

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