CYBER CRIME CASES IN WHICH CONFUSION MATRIX IS USED

Type I Error (False Positive) and Type II Error (False Negative)
Type I Error (False Positive) and Type II Error (False Negative)
Confusion Matrix
  • Stealing of personal data
  • phishing
  • Leak Your private photos
  • Hack emails for gaining information.

Confusion Matrix’s implementation in monitoring Cyber Attacks:

  • True Positive (TP): The amount of attack detected when it is actually attack.
  • True Negative (TN): The amount of normal detected when it is actually normal.
  • False Positive (FP): The amount of attack detected when it is actually normal (False alarm).
  • False Negative (FN): The amount of normal detected when it is actually attack.

Conclusion

  • It evaluates the performance of the classification models, when they make predictions on test data, and tells how good our classification model is.
  • It not only tells the error made by the classifiers but also the type of errors such as it is either type-I or type-II error.
  • With the help of the confusion matrix, we can calculate the different parameters for the model, such as accuracy, precision, etc.

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