HansITAcademy

Confusion matrix is a table is also known as the error matrix.  When a tool like the scanner or any AI or machine learning tools use algorithm and makes an estimate, you’ll get one of the four results.  True and false positives and true and false negatives.  False positive is known as type 1 error and false negative is known as type 2 error.  True positive and true negative are correctly guessed results.  Anything with false in front means it didn’t guess correctly.  False positive is when tool stated something as an attack but is good while false negative means something bad was marked as good and failed to catch it.  Think of true and false as something added to the detected positive/negative variables.