Confusion Matrix and Cyber Security

Classification Accuracy is given by the relation:


Accuracy is the number of correctly (True) predicted results out of the total.

Out of the total predicted positive values, how many were actually positivePrecision = TP / (TP + FP) = 4/5 = 0.8


Out of the total actual positive values, how many were correctly predicted as positive

Recall= TP / (TP + FN) = 4/5 = 0.8

4. F beta SCORE

Confusion matrix and accuracy

The confusion matrix that was obtained from the classifier is depicted in Figure below. It is in normalized form, since the classes are imbalanced. The darker the blue, the better the classifier is at predicting files for this class. It is clear where the classifier gets ‘confused’. The ‘identity theft’ class does not seem to do well, which has a good reason. Through reading court cases, the discovery was made that ‘platform fraud’ is linked to ‘identity theft’, as it appears that stolen identities are often used to commit platform fraud. In the confusion matrix it is shown that ‘identity theft’ is often predicted as ‘platform fraud’.

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