Confusion Matrix | Cyber Crime

What is Confusion Matrix

In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one.

What is TP / TN / FP / FN ?

True Positive (TP) :
The predicted value matches the actual value
The actual value was positive and the model predicted a positive value

True Negative (TN) :
The predicted value matches the actual value
The actual value was negative and the model predicted a negative value

False Positive (FP) – Type 1 error:
The predicted value was falsely predicted
The actual value was negative but the model predicted a positive value
Also known as the Type 1 error

False Negative (FN) – Type 2 error:
The predicted value was falsely predicted
The actual value was positive but the model predicted a negative value
Also known as the Type 2 error

Now let us Discuss what is Cyber Crime ?

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.

How Confusion Matrix has an Important take in Cyber Crimes ?

False Postivies :
False positives are mislabeled security alerts, indicating there is a threat when in actuality, there isn’t. These false/non-malicious alerts (SIEM events) increase noise for already over-worked security teams and can include software bugs, poorly written software, or unrecognized network traffic.

False Negativies :
False negatives are uncaught cyber threats — overlooked by security tooling because they’re dormant, highly sophisticated (i.e. file-less or capable of lateral movement) or the security infrastructure in place lacks the technological ability to detect these attacks.