Cyber Security involving Confusion Matrix

What is a Cybercrime?

Cybercrime is any criminal activity that involves a computer, networked device, or network.

While most cybercrimes are carried out to generate profit for the cybercriminals, some cybercrimes are carried out against computers or devices directly to damage or disable them, while others use computers or networks to spread malware, illegal information, images, or other materials. 

What is the Confusion Matrix?

This is the common word for the Machine Learning World. So, here I am relating the importance of the Confusion Matrix when it comes to Cyber Crimes. By Confusion Matrix, we can get to know how our model performs. Simply 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 above table explains clearly what Confusion Matrix is:

1. True Positive: Predicted values correctly predicted as actual positive
2. False Positive: Predicted values incorrectly predicted an actual positive. i.e., Negative values predicted as positive
3. False Negative: Positive values predicted as negative
4. True Negative: Predicted values correctly predicted as an actual negative

False Positive is also known as Type 1 error and False Negative is called Type 2 error.

We can compute the accuracy-test by using the below formula:

So, by this, we get to know clearly what the Confusion Matrix is. Now, let's discuss what made the Confusion Matrix special. Type 1 error is so dangerous.

Let’s understand this by having a case study example:

So, a company came with an AI-based Security System software that if we install, there would be no threats to our system. In this case, the Machine Learning Model gives us the output that a hacker is trying to enter into your computer but the actual reality is no one is trying to enter. In this kind of situation, we don’t need to panic as it is not very dangerous. These kinds of errors are known as False Negatives. But in one situation the machine predicted that no hacker is trying to enter and you can be safe where the reality is someone is trying to enter into our computer where we have a huge amount of data that is very important. So, this kind of error is called False Positive and very much dangerous. 

I hope you might understand the importance of two types of errors in the confusion matrix and what they mean. 

Cyber Crimes can be like:

Stealing company details

Steel bank card details 

Stealing identity 

Hacking emails and data for gaining information as data is money and a lot more.

So, the trade-off between type 1 and type 2 errors is very critical in cybersecurity. Moreover, it is crucial when it comes to Type 1 error because it causes a great loss as discussed in the above example. The problem may vary from use case to use case and sometimes it brings a great loss.