Summary
In practice, detecting attackers isn't easy. Real-life hackers are much savvier than the ones in this simulation. Attacks are also much less frequent, creating a huge class imbalance. Building machine learning models that will catch everything just isn't possible. That is why it is so vital that we work with those who have domain knowledge; they can help us squeeze some extra performance out of our models by really understanding the data and its peculiarities. No matter how experienced we become with machine learning, we should never turn down help from someone who often works with the data in question.
Our initial attempts at anomaly detection were unsupervised while we waited for the labeled data from our subject matter experts. We tried LOF and isolation forest using scikit-learn. Once we received the labeled data and performance requirements from our stakeholders, we determined that the isolation forest model was better for our data.
However, we didn&apos...