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Confusion Matrix And its Role in Cyber Security
A confusion matrix is a performance measurement for machine learning classification problem where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values. This matrix is a N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes.
A 2X2 confusion matrix would have ideally 4 elements:
The prediction is correct and is in your favor.
The prediction is right but not in your favor.
The prediction is wrong but in our favor. It is also known as type-I error and is pretty malicious or dangerous.
The prediction is wrong and also not in our favor. It is also called type-II error and is not that harmful.
Now, how does this confusion matrix affects on cyber security — —
Say , we have an intrusion detection system in a company to detect malicious attacks from outsiders . Let us say this IDS uses the concept of confusion matrix and alarms us when it detects any attack.
If any attack is actually happened , the matrix would have two outcomes — either it it would give a true positive or true negative. These wouldn’t impact much because the detection system would be activated
And if the attack has not happened , it would give out false positive and false negative. This would highly impact the organization as if the case is false positive , where the attack has actually happened and the alarm has not been raised.
That shows that confusion matrix is not 100 % accurate to detect such type of intrusions but it is still used as there are not many other ways to perform these functions.
WHY IS CONFUSION MATRIX REQUIRED??
- It shows how any classification model is confused when it makes predictions.
- The confusion matrix not only gives you insight into the errors being made by your classifier but also the types of errors that are being made.
- This breakdown helps you to overcomes the limitation of using classification accuracy alone.
- Every column of the confusion matrix represents the instances of that predicted class.
- Each row of the confusion matrix represents the instances of the actual class.
- It provides insight not only into the errors which are made by a classifier but also errors that are being made.
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