Confusion Matrix in Cyber Security

Confusion Matrix

Dear Readers,

This article is for those who want to understand the concept of Confusion Matrix in Cyber Security domain using a real life case study.

Confusion Matrix is used to describe the performance of a model by determining its accuracy, precision, etc. It uses predicted and actual values to find the accuracy, precision, recall of the algorithm.

Case : Intrusion Detection System

True Positive (TP) : The IDS detects that there is no malicious activity happening and it turns out to be true.

True Negative (TN) : The IDS detects that there is some kind of malicious activity happening and it turns out to be true.

False Positive (FP) :The IDS detects that there is no malicious activity happening, but in reality, there is some kind of malicious activity happening.. It is also called Type I Error. → VERY DANGEROUS

False Negative (FN) : Thxe IDS detects that there is some kind of malicious activity, but there is no such activity happening (Kind of FALSE ALARM). It is also called Type II Error. → FALSE ALARM

Accuracy in Confusion Matrix

Accuracy : Ratio of the : Sum of Correct Predictions to the Sum of Total Predictions

Recall in Confusion Matrix

Recall : Ratio of the
Sum of Correct Positive Predictions to the Sum of Total Positive Outcomes

Precision in Confusion Matrix

Precision : Ratio of the
Sum of Correct Positive Predictions to the Sum of Total Positive Predictions

I hope this small explanation has enough content and points to understand Confusion Matrix.

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