Cyber Security and Confusion Matrix

What is Cyber Security ?

Cyber security is the application of technologies, processes and controls to protect systems, networks, programs, devices and data from cyber attacks. It aims to reduce the risk of cyber attacks and protect against the unauthorised exploitation of systems, networks and technologies.

Cybercrime can be anything like:

• Stealing of personal data
• Identity stolen
• For stealing organizational data
• Steal bank card details.
• Hack emails for gaining information.

What is Confusion Matrix?

Confusion Matrix is a concept that is used to find the accuracy of the model that we create in Machine learning or we can explain it as a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known.

The basic terms that the Confusion matrix has are:

• True Positive [ TP ]: In TP, the Machine Learning model predicted right and it was actually right.
• True Negative [ TN ]: In TN, the Machine Learning model predicted right but actually it was the wrong prediction, also called False alarm.
• False Positive [ FP ]: In FP, the model predicts the wrong but actually it was right
• False Negative [FN ]: In FN, the model predicted wrong and actually it as wrong.

There are two types of error in the confusion matrix:

• False Negative and
• False Positive

The most dangerous error is the False Positive [FP] error as the machine predicted false but it was not false it was true. For example, the machine predicted student fails but actually student was a pass.

This error causes problems in the cybersecurity world where the tools used are based on machine learning or ai, it may give a False Negative error that may cause dangerous impacts.

Therefore the role of the confusion matrix is important in the field of machine learning.

Example of Confusion Matrix:

Confusion Matrix is a useful machine learning method which allows you to measure Recall, Precision, Accuracy, and AUC-ROC curve. Below given is an example to know the terms True Positive, True Negative, False Negative, and True Negative.

True Positive:

You projected positive and its turn out to be true. For example, you had predicted that France would win the world cup, and it won.

True Negative:

When you predicted negative, and it’s true. You had predicted that England would not win and it lost.

False Positive:

Your prediction is positive, and it is false.

You had predicted that England would win, but it lost.

False Negative:

Your prediction is negative, and result it is also false.

You had predicted that France would not win, but it won.

You should remember that we describe predicted values as either True or False or Positive and Negative.

How to prevent false positive and false negative

If you have a cybersecurity solution that generates a lot of false positives, you can send samples of the files to the solution vendor, add the files to a safe list or whitelist, or evaluate other solutions.

False negatives tend to be more dangerous. Therefore, the best way to avoid them is to keep your solution up to date, so that samples of different threats also remain current.

If you are looking to reduce false alarms specifically in email, consider Gatefy. After all, we’re experts in email security solutions, and innovation when it comes to technology.

We hope that this article has solved all your doubts about false negative and false positive, after all, there were three different examples to conceptualize the terms. In addition, we have brought the application of these terms to information security.

With that, you’re now able to identify occurrences and search for solutions to give more efficiency to your daily life.

Thanks for Reading!! 😁🙌