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The promise of every security solution is to detect the next attack, but verifying that claim is almost impossible. Attacks are extremely rare and tend to change: the ability to catch attacks that happened in the past say little about the ability to find things that will happen in the future and those breached are unlikely to share information and data about how that happened. In this presentation I will show the different approaches and metrics we found to measure the efficiency of the unsupervised machine learning algorithms commonly used in UBA products.
The promise of every security solution is to detect the next attack, but verifying that claim is almost impossible. Attacks are extremely rare and tend to change: the ability to catch attacks that happened in the past say little about the ability to find things that will happen in the future and those breached are unlikely to share information and data about how that happened. In this presentation I will show the different approaches and metrics we found to measure the efficiency of the unsupervised machine learning algorithms commonly used in UBA products.
