Identifying Stages of ML Lifecycle Targeted by Adversarial Attacks

An ML system’s lifecycle includes several stages. Different adversarial attacks in our sample targeted different stages of the targeted ML system’s lifecycle. For example, poisoning attacks, which directly manipulate an ML model’s training data occurs within the data collection or training stages. We aimed to understand which attacks occur in which stage of the ML lifecycle to inform where the mitigations might be instituted.

• Data collection stage consists of retrieving the data, identifying issues in the data and making modifications, and improving the data quality before it is used as input to ML model.

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• Design stages refers to choosing the type of model, defining its parameters, and defining any other specific characteristics the ML system should have: e.g., whether the ML system will use real-time training or not.

• Training stage is where data scientist trains the ML model(s) to accomplish a specific learning task such as classification or detection.

• Evaluation stage consists of testing the model on unknown or out of sample data, hyperparameter tuning the model to get better results on out of sample data, and defining the best accuracy measures for the model to be analyzed: e.g., accuracy, area under the curve, F-1 score, etc.

• Deployment stage is where the model is put into the production environment. • Monitoring stage consists of using the ML system in its production environment, supervising the

performance of the model, and taking necessary actions to improve the model.

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