Cyber Security and Confusion Matrix

How confusion matrix help to make our machine learning model more accurate and how it is important for cyber security world.

What is machine learning?

How does machine learning work?

What is a cyber attack?

Need of AI to prevent cyber attacks

Do AI is capable of preventing Cyber attacks with 100% accuracy?

So here is one tool called Confusion matrix which tells us about the accuracy of our machine learning model.

Confusion Matrix?

T → true predicted

F → false predicted

P → positive

N → Negative

Let’s suppose positive means the prediction is in our favour and negative means against us.

TP → means the prediction is in our favour and it is true i.e the actual output is in our favour.

TN → means the prediction is against us and it is true i.e the actual output is also against us.

FP → means the prediction is in our favour and it is false i.e the actual output is against us.

FN → means the prediction is against us and it is negative i.e. the actual output is in our favour.

So FP is the most important factor that the confusion matrix tells us because it tells us how many predictions are made wrong and they are actually against us. But the model will show them in our favour and we will not be aware of this. This may cause huge losses to us.

Let’s understand Type 1 Error and Type 2 Error errors with an abasic example.

Face Detection In Mobile Phones:

like if the system is not detecting the right person every time then it may lead to a bad user experience but there is no security issue. This type of error is known as Type 2 Error i.e False negative(FN).

But if the system is detecting the wrong person and giving access to it, then this will lead to security issue. This error is known as Type 1 Error i.e False Positive(FP) and it is against us.

So industries are more concerned about Type 1 Error.

likewise, industries are using AI to prevent cyber attacks on their server and try to minimise Type 1 Error.

CONCLUSION