Application of AI in Intrusion Detection System 👨‍💻

Intrusion Detection System (IDS)

Confusion Matrix

Confusion matrix of binary classification:

  • True Positive: These are the patients that had heart disease that was correctly identified by the model.
  • True Negative: These are the patients that did not have heart disease that was correctly identified by the model.
  • False Negative: False-negative is when a patient has heart disease, but the model said they didn’t.
  • False Positive: False positive are the patients that do not have heart disease, but the model says they do have.

How ML is helpful to improve the performance of IDS?

Artificial Intelligence based IDS

ML Algorithms

Deep learning algorithms

Confusion Matrix

  1. True Positive (TP): The data instances correctly predicted as an Attack by the classifier.
  2. False Negative (FN): The data instances wrongly predicted as Normal instances.
  3. False Positive (FP): The data instances wrongly classified as an Attack.
  4. True Negative (TN): The instances correctly classified as Normal instances.
  • Precision: It is the ratio of correctly predicted Attacks to all the samples predicted as Attacks.
  • Recall: It is a ratio of all samples correctly classified as Attacks to all the samples that are actually Attacks. It is also called a Detection Rate.
  • False alarm rate: It is also called the false positive rate and is defined as the ratio of wrongly predicted Attack samples to all the samples that are Normal.
  • True negative rate: It is defined as the ratio of the number of correctly classified Normal samples to all the samples that are Normal.
  • Accuracy: It is the ratio of correctly classified instances to the total number of instances. It is also called Detection Accuracy and is a useful performance measure only when a dataset is balanced.
  • F-Measure: It is defined as the harmonic mean of the Precision and Recall. In other words, it is a statistical technique for examining the accuracy of a system by considering both the precision and recall of the system.

Conclusion:

Thank You for reading!! 😇😇

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