Cyber crime cases and Confusion matrix

Confusion matrix for the result of a Binary Classifier
  • There are two possible predicted classes: “yes” and “no”. If we were predicting the presence of a disease, for example, “yes” would mean they have the disease, and “no” would mean they don’t have the disease.
  • The classifier made a total of 165 predictions (e.g., 165 patients were being tested for the presence of that disease).
  • Out of those 165 cases, the classifier predicted “yes” 110 times, and “no” 55 times.
  • In reality, 105 patients in the sample have the disease, and 60 patients do not.
  • true positives (TP): These are cases in which we predicted yes (they have the disease), and they do have the disease.
  • true negatives (TN): We predicted no, and they don’t have the disease.
  • false positives (FP): We predicted yes, but they don’t actually have the disease. (Also known as a “Type I error.”)
  • false negatives (FN): We predicted no, but they actually do have the disease. (Also known as a “Type II error.”)
  • Malware. Malware is a term used to describe malicious software, including spyware, ransomware, viruses, and worms.
  • Phishing.
  • Man-in-the-middle attack.
  • Denial-of-service attack.
  • SQL injection.
  • Zero-day exploit.
  • DNS Tunneling.
  • using key and certificates in ssh login to avoid spof.
  • End to end encryption which greatly help to avoid attacks.
  • automating the process of cyber attack monitoring.
  • using a dedicated hardware firewall server.
  • using encrypted cloud storage.

False Negative —

It means that the result is actually true but the system returned false ,

False Positive -

It means the result is actually false but due to the system inefficiency the result shown to you True.

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