🎓Cyber Crime & Confusion Matrix

Cybercrime, or computer crime, 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.

Most, but not all, cybercrime is committed by cybercriminals or hackers who want to make money or profit. Cybercrime is carried out by individuals or organizations.

Some cybercriminals are organized, use advanced techniques and are highly technically skilled. Others are novice hackers.

📗Confusion Matrix:

A confusion matrix is a type of table construct that plays a specific role in machine learning and related engineering. It helps to show the prediction and recall in a system where the values of the test data are known.

In the confusion matrix:

A true positive exists where observation is positive with a positive prediction.
A false positive exists where observation is negative, with a positive prediction.
A true negative exists where observation is negative with negative prediction.
A false negative indicates a positive observation with a negative prediction.

1.Accuracy is used to find the portion of correctly classified values. It tells us how often our classifier is right. It is the sum of all true values divided by total values.

Accuracy = (TP + TN)/(TP + TN + FP + FN).

2. Precision is used to calculate the model’s ability to classify positive values correctly. It is the true positives divided by the total number of predicted positive values.

Precision: (True positives / Predicted positives) = TP / TP + FP.

3. Recall is used to calculate the model’s ability to predict positive values. It is the true positives divided by the total number of actual positive values.

Recall: (True positives / all actual positives) = TP / TP + FN.

4.F1-score is the harmonic mean of Recall and Precision. It is useful when you need to take both Precision and Recall into account.

F1-score:((2*recall*precision )/ (recall + precision))=(2*(TP/TP+FN)*(TP/TP+FP))/((TP/TP+FN)+(TP/TP+FP))

5. Specificity: (True negatives / all actual negatives) =TN / TN + FP.

6. Misclassification: (all incorrect / all) = FP + FN / TP + TN + FP + FN.
also calculated as -> 1-Accuracy.

📘How to protect yourself against cybercrime:

1. Keep software and operating system updated.
   2. Use anti-virus software and keep it updated.
   3. Use strong passwords.
   4. Never open attachments in spam emails.
   5. Do not click on links in spam emails or untrusted websites.
   6. Do not give out personal information unless secure.
   7. Contact companies directly about suspicious requests.
   8. Be mindful of which website URLs you visit.
   9. Keep an eye on your bank statements.

📕Confusion Matrix Implementation in Cyber Crime:

Conclusion:

📒Types of Error:

Type1 error:

This type of error can prove to be very dangerous. Our system predicted no attack but in real attack takes place, in that case no notification would have reached the security team and nothing can be done to prevent it.

Type2 error:

This type of error are not very dangerous as our system is protected in reality but model predicted an attack. the team would get notified and check for any malicious activity. This doesn’t cause any harm.