Cyber Crime and Confusion Matrix

Hello Readers, In this blog we are going to talk about role of Confusion Matrix in Cyber Security .

So let’s first understand what is confusion matrix.

Confusion Matrix ?

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. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.

Let’s understand these terms

True Positive: You predicted positive and it’s true.

True Negative: You predicted negative and it’s true.

False Positive: (Type 1 Error) You predicted positive and it’s false.

False Negative: (Type 2 Error) You predicted negative and it’s false.

So this would give an idea of what the four boxes in the confusion matrix are representing.

Why you need Confusion matrix?

Here are pros/benefits of using a confusion matrix.

• It shows how any classification model is confused when it makes predictions.
• Confusion matrix not only gives you insight into the errors being made by your classifier but also 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 the errors which are made by a classifier but also errors that are being made.

What is a cyber attack?

A cyber attack is an attempt to gain unauthorized access to a computer, computing system or computer network with the intent to cause damage. Cyber attacks aim to disable, disrupt, destroy or control computer systems or to alter, block, delete, manipulate or steal the data held within these systems.

Face Detection In Mobile Phones:

As we know, face detection system in mobile phones is using AI behind the scene. Since it is AI so there may be some vulnerabilities.

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:

A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier. It is used to measure the performance of a classification model. It can be used to evaluate the performance of a classification model through the calculation of
performance metrics like accuracy, precision, recall, and F1-score.

Need for Confusion Matrix in Machine learning:
1. It evaluates the performance of the classification models, when they make predictions on test data, and tells how good our classification model is.
2. It not only tells the error made by the classifiers but also the type of errors such as it is either type-I or type-II error.
3. With the help of the confusion matrix, we can calculate the different parameters for the model, such as accuracy, precision, etc.

Thank You !!