Cyber Crime Cases and Confusion Matrix
In this article, we will discuss cybercrime cases where we talk about the confusion matrix
As we all know that cybersecurity is very important in today’s time as there are a lot of cybercrimes happening due to an increase in online business transactions, etc. So here I would discuss the importance of a confusion matrix when considering cyber crimes.
What is Cyber Security?
Cybersecurity is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. It’s also known as information technology security or electronic information security.
The global cyber threat continues to evolve at a rapid pace, with a rising number of data breaches each year. There are three types of cyber threats:
- Cybercrime
- Cyberattack
- Cyber terrorism
There are many ways in which cyber crimes are happening. Some of them are:
- Stealing personal data
- Stealing organizational details
- Stealing bank card details
- Hack emails for gaining information
What is Confusion Matrix?
The confusion matrix is a concept that is used to find the accuracy of the model that we create in Machine learning which is used to determine the performance of the classification model for a series of test data provided. This can only be resolved if the actual value for the data test is known. The matrix itself can be easily understood, but related terminology may be confusing as it shows an error in the performance of the model in the form of a matrix, therefore also known as an error matrix.
The matrix is divided into two dimensions, which are predicted values and actual values along with the total number of predictions.
Some basic terms of confusion matrix are:
- True Positive: The model predicted right and it was actually true.
- True Negative: The model predicted right but actually it was the wrong prediction.
- False Positive: The model predicts the wrong but actually it was right.
- False Negative: The model predicted wrong and actually it was wrong.
There are two types of errors in the confusion matrix:
1. False Positive(Type I error):
It is the most dangerous error as the machine predicted the false but actually, it was true. eg: Suppose the machine predicted that a student fails in an exam but actually the student was a pass. So it may occur a lot of problems for that student. This error causes problems in the cybersecurity world where the tools used are based on machine learning or Artificial Intelligence that may cause a dangerous impact.
2. False Negative(Type II error):
It gives negative means attack alert. But the model incorrectly predicts the negative class so it means safe.
The trade-off between type 1 and type 2 errors is very critical in cyber security. Let’s take an example.
A new person comes and tries to log himself in. The recognition system makes an error and allows him in. Now, this is very dangerous. An unauthorized person has made an entry. This could be very damaging to the whole company. In both cases, there was an error made by the security system. But the tolerance for False Negative here is 0 although we can still bear False Positive.
This shows the critical nature that might vary from use case to use case where we want a tradeoff between the two types of error.