A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known. In simple words, we use a confusion matrix to compare the accuracy of the predicted value of the classification model with the actual value of the dataset.
We have four sections in the confusion matrix:-
a) True Negatives (TN): It means the predicted value is negative which is the same as the actual value.
b) False Positive (FP): It means the predicted value is positive but the actual value is negative.
c) False Negatives (FN): It means the predicted value is negative but the actual value is positive.
d) True Positives (TP): It means the predicted value is positive which is the same as the actual value.
Many cybercrimes can take place by the two types of error in the confusion matrix :
- Type I Error (FP)
- Type II Error (FN)
There are four possible states in Intrusion Detection Sytems (IDS)for each activity observed. A true positive state is when the IDS identifies an activity as an attack and the activity is actually an attack. A true positive is a successful identification of an attack. A true negative state is similar. This is when the IDS identifies an activity as acceptable behavior and the activity is actually acceptable. A true negative is successfully ignoring acceptable behavior. Neither of these states is harmful as the IDS is performing as expected. A false positive state is when the IDS identifies an activity as an attack but the activity is acceptable behavior. A false positive is a false alarm. A false negative state is the most serious and dangerous state. This is when the IDS identifies an activity as acceptable when the activity is actually an attack. That is, a false negative is when the IDS fails to catch an attack. This is the most dangerous state since the security professional has no idea that an attack took place. False positives, on the other hand, are an inconvenience at best and can cause significant issues. However, with the right amount of overhead, false positives can be successfully adjudicated; false negatives cannot.
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