How Confusion Matrix handles Cybercrime!

What is Cybercrime?

Cybercrime is a crime that involves a computer and a network. The computer may have been used in the commission of a crime, or it may be the target. Cybercrime may harm someone’s security and financial health.

There are many concerns surrounding cybercrime when confidential information is intercepted or disclosed, lawfully or otherwise. Phishing, Identity theft, Hacking, Spreading hate and terrorism, etc. are some of the types of Cybercrime.

What is Confusion Matrix?

So, in Data Science, after Data Pre-processing and building the model based on that data, we test the model on testing data. Now, the confusion matrix tells how accurately or precisely the model has been trained and how well the model performs when exposed to new data.

Fields of Confusion Matrix

  1. True Positive (TP)
    — The predicted value matches the actual value
    — The actual value was positive (i.e yes) and the model predicted a positive value (i.e yes)
  2. True Negative (TN)
    — The predicted value matches the actual value
    — The actual value was negative(i.e no) and the model predicted a negative value (i.e no)
  3. False Positive (FP) — Type 1 error
    — The predicted value doesn’t match the actual value
    — The actual value was negative(i.e no) but the model predicted a positive value(i.e yes)

False Negative (FN) — Type 2 error
— The predicted value doesn’t match the actual value
— The actual value was positive(i.e yes) but the model predicted a negative value(i.e no)

So, where the Confusion Matrix comes into play when dealing with Cyber Security?

All the MNCs have their data on a server. And, in that server, many Machine Learning models are deployed so that when a new attack takes place, it can warn the concerned engineers. Now, these models are trained on a very large set of data and they are exposed to a very large number of types of possible attacks.

Now, the model classifies the attack and then engineers evaluate them as one of the classes of Confusion Matrix, i.e. TP, TN, FP, FN. Now, these results keep on adding into the data set on which the ML models are trained, and when there is a possibility of an actual attack, it can make better and better predictions.

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