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True positives

(also TP)

True positives definition

True positives are instances where a positive condition, such as an intrusion or attack, is correctly identified. In simpler terms, a true positive occurs when a threat is predicted to be present and is found to actually be present. True positives are often used to evaluate the performance of algorithms, systems, or tests, particularly in the fields of machine learning and cybersecurity.

True positives examples

  • In an intrusion detection system, true positives are the number of times the system accurately identifies an unauthorized access attempt.
  • In a spam filter, true positives are the instances where the system correctly identifies and blocks an unwanted email.

Comparing true positives to other similar terms

  • True negatives: These are instances where a negative condition, such as the absence of an attack, is correctly identified.
  • False positives: These occur when a positive condition is incorrectly identified, such as when a legitimate user or email is mistakenly flagged as a threat.
  • False negatives: These are instances where a negative condition is incorrectly identified, such as when an actual threat goes undetected.

Weighing the pros and cons

True positives are an essential metric for evaluating the effectiveness of network security systems. High true positive rates indicate accurate and reliable performance. However, focusing solely on true positives may lead to overlooking false positives or false negatives, which could also impact the overall performance and efficiency of a system.

Tips for improving true positives

  • Fine-tune algorithms or systems to reduce the likelihood of false positives and false negatives.
  • Regularly update and validate models or databases used for detection or classification tasks.
  • Use multiple complementary methods or tools to increase the chances of accurate detection or classification.