Role of Confusion Matrix in Cyber Security

If you put a key under the mat for the cops, a burglar can find it, too. Criminals are using every technology tool at their disposal to hack into people’s accounts. If they know there’s a key hidden somewhere, they won’t stop until they find it. ~Tim Cook

What is Cyber Security ? 🤔

Cyber security 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 term applies in a variety of contexts, from business to mobile computing, and can be divided into a few common categories.

· Network security is the practice of securing a computer network from intruders, whether targeted attackers or opportunistic malware.

· Application security focuses on keeping software and devices free of threats. A compromised application could provide access to the data its designed to protect. Successful security begins in the design stage, well before a program or device is deployed.

· Information security protects the integrity and privacy of data, both in storage and in transit.

· Operational security includes the processes and decisions for handling and protecting data assets. The permissions users have when accessing a network and the procedures that determine how and where data may be stored or shared all fall under this umbrella.

In this Security world Confusion Matrix plays a vital role

Then What is Confusion Matrix ?

A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making.

For a binary classification problem, we would have a 2 x 2 matrix as shown below with 4 values:

Let’s decipher the matrix:

  • The target variable has two values: Positive or Negative
  • The columns represent the actual values of the target variable
  • The rows represent the predicted values of the target variable

But wait — what’s TP, FP, FN and TN here? That’s the crucial part of a confusion matrix. Let’s understand each term below.

Understanding True Positive, True Negative, False Positive and False Negative in a Confusion Matrix

True Positive (TP)

  • The predicted value matches the actual value
  • The actual value was positive and the model predicted a positive value

True Negative (TN)

  • The predicted value matches the actual value
  • The actual value was negative and the model predicted a negative value

False Positive (FP) — Type 1 error

  • The predicted value was falsely predicted
  • The actual value was negative but the model predicted a positive value
  • Also known as the Type 1 error

This is the most critical value because actually cyber attack happened and machine learning model haven’t informed the organization. And this causes huge losses to the organization. Because they are not able to get information at the right time and they haven’t taken any immediate action after the attack happened

False Negative (FN) — Type 2 error

  • The predicted value was falsely predicted
  • The actual value was positive but the model predicted a negative value
  • Also known as the Type 2 error

Conclusion

By this we can conclude that Machine Learning has a very vital role in terms of Security System, were it can protect the Data of the companies which is very crucial. Also i have explained the role of confusion matrix in the cyber security world..

I hope this helped you, if you have any further queries you can contact me on my LinkedIn.

https://www.linkedin.com/in/dileepkumarsr/

Passionate learner || Data Science Enthusiast || Django || Competitive Programmer