Role of Confusion Matrix in Cyber Crime

What is Cyber Crime?

Cybercrime, also called computer crime, the use of a computeras an instrument to further illegal ends, such as committing fraud, trafficking in child pornography and intellectual property, stealing identities, or violating privacy. Cybercrime, especially through the Internet, has grown in importance as the computer has become central to commerce, entertainment, and government.

Types Of Cybercrime:

1. Email and internet fraud.
2. Identity fraud.
3. Theft of financial or card payment data.
4. Theft and sale of corporate data.
5. Ransomware attacks.

What is Confusion Matrix?

A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making.

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.

Lets understand the above term given in the figure:

True Positive (TP) :- Model Predicted True and Actual Value is also True .

True Negative (TN) :- Model Predicted False and Actual Value is also False.

False Positive (FP) :- Model Predicted True and Actual Value is also False

False Negatives (FN) :- Model Predicted False and Actual Value is also True

How Confusion Matrix can helps in Cyber Security Field to tackle with Cyber Attacks ?

1. Confusion matrix not only gives you insight into the errors being made by your classifier but also types of errors that are being made.
2. Every column of the confusion matrix represents the instances of that predicted class.
3. Each row of the confusion matrix represents the instances of the actual class.
4. It shows how any classification model is confused when it makes predictions.
5. To design the IDS we can use multiple machine learning algorithms but the question is which algorithm is most accurate in cyber attack detection as well as require less time.To find the answer of this question we need to test and compare the results of various algorithms and here comes the role of “Confusion Matrix”.

How To Deal With False Positive and False Negative?

A False Positive Error, or False Positive, is a result that indicates a given condition exists when it does not. For example, a pregnancy test which indicates a woman is pregnant when she is not, or the conviction of an innocent person.

False Positive and False Negative are technical errorsand thus, need close investigation of the system used in tests. In order to solve the problem of False Positive, it is important that the IDS configuration is checked and reviewed

These false alarms account for roughly 40% of the alerts cybersecurity teams receive on a daily basis and at large organizations can be overwhelming and a huge waste of time.

False negatives are uncaught cyber threats overlooked by security tooling because they’re dormant, highly sophisticated or the security infrastructure in place lacks the technological ability to detect these attacks.

What Does a Confusion Matrix Tell You?

A Confusion Matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model.

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