According to Security Magazine, there are over 2,200 attacks each day which breaks down to nearly 1 cyberattack every 39 seconds.

Many Question we can resolved after reading this blog…


Cybersecurity is the protection of internet-connected systems such as hardware, software and data from cyberthreats. The practice is used by individuals and enterprises to protect against unauthorized access to data centers and other computerized systems.

A strong cybersecurity strategy can provide a good security posture against malicious attacks designed to access, alter, delete, destroy or extort an organization’s or user’s systems and sensitive data. Cybersecurity is also instrumental in preventing attacks that aim to disable or disrupt a system’s or device’s operations.

Common types of cyber attacks

  • Malware
  • Phishing
  • Man-in-the-middle attack
  • Denial-of-service attack
  • SQL injection
  • Zero-day exploit
  • DNS Tunneling


With an increasing number of users, devices and programs in the modern enterprise, combined with the increased deluge of data, much of which is sensitive or confidential, the importance of cybersecurity continues to grow. The growing volume and sophistication of cyber attackers and attack techniques compound the problem even further.

In order to provide cybersecurity, we need to understand CONFUSION MATRIX & its Importance. 🤔 …WHY?


A confusion matrix is a performance measurement technique for Machine learning classification. It is a kind of table which helps you to the know the performance of the classification model on a set of test data for that the true values are known. The term confusion matrix itself is very simple, but its related terminology can be a little confusing.

Look at this below meme….It is really funny as well as confusing. 😂


Why CONFUSING? Things look right but not as it seems. So Let’s try to understand the meaning of that meme. For this, we need to understand the four outcomes of the confusion matrix.

Four outcomes of the confusion matrix

The confusion matrix visualizes the accuracy of a classifier by comparing the actual and predicted classes. The binary confusion matrix is composed of squares:

Now let see what we can learn from this matrix?

  • There are two possible classifications: “yes” and “no”. If we have to predict the presence of an occurrence than “yes” would mean that that event has occurred and “no” would mean the opposite i.e. it didn’t happen.
  • The model made a total of 100 predictions about an event
  • Out of those 100 results the model predicted “yes” 45 time and “no” 55 times. Where as in reality 60 times the event in question occurred and 40 times it didn’t

Basic Terms to keep in mind when using the confusion matrix

  • True Positives (TP): These are cases in which we predicted yes (they have the disease), and they do have the disease.
  • True Negatives (TN): We predicted no, and they don’t have the disease.
  • False Positives (FP): We predicted yes, but they don’t actually have the disease. (Also known as a “Type I error.”)
  • False Negatives (FN): We predicted no, but they actually do have the disease. (Also known as a “Type II error.”)

So, Now we can judged what that meme really seems to say…

Actual meaning of meme

(1) First picture is the case of Type I Error.

(2) Second picture is the case Type II Error.


Consider cyberattacks happened in the orangization, but Machine Learning Model haven’t informed which results huge loss to the organization in terms of data security, data privacy and Trust. And this was happened because Model failed to predict the Threats, simply known as False Negative Case.

But when cyberattacks were not happened but model predict that it is happened. This results to unnecessary waste your precious time. This is case of False Positive Case.


From this article, we can conclude that Machine Learning has played a very crucial role in the Organizations, where data security, data privacy and cybersecurity has really important. We need to understand the importance of Confusion Matrix and its two types of errors while building any machine learning model.

I hope you find some insightful here…🤩