Confusion Matrix & Cyber Crime

What is Confusion Matrix?

A confusion matrix is a performance measurement technique for Machine learning classification. It is a kind of table which helps you to the know the performance of the classification model on a set of test data for that the true values are known.

For a binary classification problem, we would have a 2 x 2 matrix as shown below with 4 values:

Let’s decipher the matrix:

• The target variable has two values: Positive or Negative
• The columns represent the actual values of the target variable
• The rows represent the predicted values of the target variable.

Outcomes of the confusion matrix

• TP: True Positive: Predicted values correctly predicted as actual positive
• FP: Predicted values incorrectly predicted an actual positive. i.e., Negative values predicted as positive
• FN: False Negative: Positive values predicted as negative

• TN: True Negative: Predicted values correctly predicted as an actual negative

Cyber Security And Machine Learning

The popularity of the Web is continuously rising and our daily lives are more and more dependent on this source of information. Accordingly, the Hypertext Transfer Protocol (HTTP) has evolved to one of the most employed application layer protocols in the Internet. But with increasing global dependence on the Web, attackers are even more interested in tampering with those systems.

Types of Cyber Crime :-

• Phishing :- using fake email messages to get personal information from internet users.
• Hacking :- shutting down or misusing websites or computer networks
• Spreading hate and inciting terrorism

Why understanding False Positives and False Negatives is important in Cyber Security ?

Understanding the differences between false positives and false negatives, and how they’re related to cybersecurity is important for anyone working in information security. Why? Investigating false positives is a waste of time as well as resources and distracts your team from focusing on real cyber incidents (alerts) originating from your SIEM.

On the flip side, missing false negatives (uncaught threats) increases your cyber risk, reduces your ability respond to those attackers, and in the event of a data breach, could lead to the end of your business…